From 6486037392969f196a696c1fbfb8180ef80d7672 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 30 Sep 2026 09:20:38 -0400 Subject: [PATCH 01/15] Record the judge-isolation audit of the published verdicts Binds each of the 1,342 verdicts in release 20260929's audit tree and the previous release's tree to the transcript that produced it, and lists every tool call. One published judge used tools (086 federal refundable credits), one saw its case's exclusion history through git status (045 snap), and three Codex verdicts have no transcript that holds their text. An independent re-implementation agrees on every row. Co-Authored-By: Claude Opus 5.5 --- docs/judge_isolation_2026-09-30/REPORT.md | 154 + .../audit_judge_isolation.py | 671 +++++ .../codex_prompt_identity.json | 2446 +++++++++++++++ .../independent/audit_indep.py | 522 ++++ .../independent/build_index.py | 87 + .../independent/crosscheck_walk.py | 47 + .../independent/finalize.py | 165 + .../independent/summary_table.md | 10 + .../independent/transcripts.py | 239 ++ .../independent/verdicts_indep.csv.gz | Bin 0 -> 52986 bytes .../review_independent.md | 76 + .../review_redteam.md | 81 + .../session_contexts.json | 2667 +++++++++++++++++ .../judge_isolation_2026-09-30/tool_calls.csv | 468 +++ docs/judge_isolation_2026-09-30/verdicts.csv | 1343 +++++++++ 15 files changed, 8976 insertions(+) create mode 100644 docs/judge_isolation_2026-09-30/REPORT.md create mode 100644 docs/judge_isolation_2026-09-30/audit_judge_isolation.py create mode 100644 docs/judge_isolation_2026-09-30/codex_prompt_identity.json create mode 100644 docs/judge_isolation_2026-09-30/independent/audit_indep.py create mode 100644 docs/judge_isolation_2026-09-30/independent/build_index.py create mode 100644 docs/judge_isolation_2026-09-30/independent/crosscheck_walk.py create mode 100644 docs/judge_isolation_2026-09-30/independent/finalize.py create mode 100644 docs/judge_isolation_2026-09-30/independent/summary_table.md create mode 100644 docs/judge_isolation_2026-09-30/independent/transcripts.py create mode 100644 docs/judge_isolation_2026-09-30/independent/verdicts_indep.csv.gz create mode 100644 docs/judge_isolation_2026-09-30/review_independent.md create mode 100644 docs/judge_isolation_2026-09-30/review_redteam.md create mode 100644 docs/judge_isolation_2026-09-30/session_contexts.json create mode 100644 docs/judge_isolation_2026-09-30/tool_calls.csv create mode 100644 docs/judge_isolation_2026-09-30/verdicts.csv diff --git a/docs/judge_isolation_2026-09-30/REPORT.md b/docs/judge_isolation_2026-09-30/REPORT.md new file mode 100644 index 00000000..c1f952f4 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/REPORT.md @@ -0,0 +1,154 @@ +# Judge isolation audit of the published PolicyBench verdicts (2026-09-30) + +## Question + +On 2026-09-30, during the judging for GPT-6.1 Sol's release, 9 of 134 Opus 5.5 judges read the repo's adjudication decision records and 8 read the policyengine-us checkout. The old `scripts/run_audit_claude.sh` ran each judge from inside the repo with the file and search tools enabled. This audit asks whether any judge behind a verdict in the published release did the same: read files beyond its own prompt, searched, used a shell or browsed. + +Trees audited: +- **seed**: the 674 judged cases of release `dashboard-data-20260929`, at the local stage `adds0928-stage2/results/local/adds0928-v3/audit`. GPT-6.1 Sol's release carries 540 of them over byte for byte. +- **unified**: the 668 judged cases of the previous release tree, at the local `results/local/unified_audit/audit`. A unified verdict is still published only if the seed holds the same bytes. + +## Method + +[`audit_judge_isolation.py`](audit_judge_isolation.py) binds every verdict to the transcript that produced it and classifies every tool call in that transcript. It assumes nothing about what a judge was able to do. + +- **Binding.** A verdict counts as bound only when the answer in its transcript equals the verdict, compared as parsed JSON through the writers' own serialisation (`json.dumps(indent=2, sort_keys=True)`). + - Codex judges: the final message in the case's `codex.log`. + - `run_audit_claude.sh` judges: the `StructuredOutput` call in the session transcript named by the sidecar's session id. Sessions were searched in `~/.claude/projects`, every subfleet login's config and Logpile's copies. + - Workflow-subagent judges: the `StructuredOutput` call of the agent whose journal label names the case, across the 21 workflow runs whose journals name a PolicyBench case. Every verdict matched exactly one agent. +- **Tool calls.** Every `tool_use` content part counts (server and MCP tools included), except the final `StructuredOutput`. When a judge launched an Agent, Task or Workflow, its delegates' transcripts are classified too, and their calls count as the judge's. For Codex, every `exec` block and every `mcp:`, `web search:` or patch line counts. Codex 0.144.0, the judges' version, prints all of these; other logs from that version on this machine show them. +- **Context.** For each judge the audit also records: + - the working directory, and whether it is a git checkout; + - every CLAUDE.md or memory file injected, with its text as injected (kept with the local audit); + - any skill loaded into the conversation; + - whether the judge saw exactly the published `prompt.md`. For `claude -p` judges that is the stdin message; for workflow judges, the numbered lines their Read returned; for Codex, the echoed prompt. + +**Validation.** On the GPT-6.1 Sol job's own 139 verdict-producing transcripts, the classifier flags exactly 18 judges. That matches the 9 decision-record readers, the 1 repo searcher and the 8 policyengine-us readers named in that job's `judge_report.md`. + +**Differential check.** An independent re-implementation, written from scratch on an Opus 5.5 lane without access to this audit ([`independent/`](independent/), report [`review_independent.md`](review_independent.md)), agrees on all 1,342 rows: +- the affected label (3 yes, 6 unknown, 1,333 no); +- liveness; +- the exact transcript path of every bound verdict. + +A second Opus reviewer inside that job re-checked all 1,333 "no" rows and refuted none. + +An adversarial review on an Opus 5.5 lane ([`review_redteam.md`](review_redteam.md)) found no second affected verdict. It did find weaknesses in how the first version counted and described things, all fixed here: +- `us_case_notes.csv` and row-annotation reads now count as prior-verdict reads; +- Codex `mcp:` and `web search:` lines are now detected; +- injected nested CLAUDE.md content is now captured; +- git status is now checked directly, since `gitBranch: "HEAD"` appears even outside a repository; +- prompt identity is now verified. + +## Results + +Counts by wave. The seed is the published release; unified rows count all 668 verdicts, and "live" means the same verdict is also in the seed. + +| Tree | Wave | Runner | Verdicts | Live | Bound to transcript | Used tools | Transcript missing | +|---|---|---|---|---|---|---|---| +| seed | Codex (Aug; sidecars backfilled) | run_audit_codex.sh, gpt-5.6-sol | 303 | 303 | 300 | 0 | **3** | +| seed | 2026-09-05 | run_audit_claude.sh, Opus 5 | 132 | 132 | 132 | **1** | 0 | +| seed | 2026-09-22 | 16 workflow subagents, 1 run_audit_claude.sh | 17 | 17 | 17 | 0 | 0 | +| seed | 2026-09-29 | 221 workflow subagents, 1 run_audit_claude.sh | 222 | 222 | 222 | 0 | 0 | +| unified | Codex | run_audit_codex.sh | 314 | 303 | 311 | 0 | 3 (the same 3) | +| unified | 2026-09-05 | run_audit_claude.sh | 183 | 132 | 183 | 1 (the same 086) | 0 | +| unified | 2026-09-22 | 168 workflow, 2 run_audit_claude.sh, 1 main session | 171 | 17 | 170 | 1 main-session verdict (not live) | 0 | + +The 2026-09-22 wave spans judgments dated September 22 to 24. The 2026-09-29 wave's run_audit_claude.sh verdict is `082 payroll_tax`, and the 2026-09-22 wave's is `045 snap`. Both ran in a git worktree with Read, Grep and Glob enabled, and both made no tool call. + +### Affected: one published verdict + +**`us__scenario_086__federal_refundable_credits`** (2026-09-05 wave, Claude Opus 5 through run_audit_claude.sh, live in the seed and carried into GPT-6.1 Sol's release). + +The judge ran from the `policybench-wt/newmodels` git worktree. It launched a background Workflow (`verify-actc-diagnosis`, `wf_3ac730e3-7e7`), loaded TaskOutput through ToolSearch and read the workflow's output. The workflow's four agents made 28 calls between them: +- Grep over the repo (patterns `refundable`, `12824`, `eitc`, `24150`, `scenario_086,federal_refundable_credits` and others). +- Read of `annotations/.../us_case_reference_explanations.csv`, `paper/snapshot/20260501/us_scenarios.csv` and `.../reference_outputs.csv`. +- Grep hits in `us_audit_row_annotations.csv` returned the earlier published diagnoses for **this same case**, for 9 of its models. +- A grep of `us_case_notes.csv` returned this case's earlier published case note: `llm_error / credit_phaseout`, 10 wrong models. + +The published verdict repeats that class. It is not independent of the earlier verdict, and it must be re-judged. + +### Transcript missing: three published verdicts + +The published text of these three Codex verdicts appears in no transcript on this machine: +- `us__scenario_014__federal_refundable_credits` +- `us__scenario_015__federal_refundable_credits` +- `us__scenario_015__head_medicaid_eligible` + +Each case's `codex.log` holds an answer with the same classes but different wording. The runner rewrites the log and the verdict together, so it cannot leave them out of step. Something after the logged runs replaced these three verdicts, and no record says what. Their sidecars say `backfilled_from: codex.log`, a claim that log does not support. The Codex runner passed `--ephemeral`, so no Codex session store holds them. + +The independent re-implementation traced why, in session `3c78d187`: +- At 2026-08-18 01:25:58Z, a `pkill -f run_audit_codex` stopped in-flight Codex re-judges that had already overwritten these three logs. +- At 01:27:48Z, the same session restored 381 `verdict.json` files from Time Machine snapshot `2026-08-17-194721`. + +The published verdicts' own logs existed only in that snapshot. The only local snapshot left is from 2026-09-30, and this session is not permitted to read the Time Machine volume. The restore is also why 469 Codex sidecars (in both trees) give 01:27:48Z as their `judged_at_utc`. + +Whether these judges used tools cannot be established. They are re-judged with the hardened runner so that the published text carries a bound transcript. None of the three has an adjudication record. + +### Exposed through context, without a tool call: one published verdict + +**`us__scenario_045__snap`** (2026-09-24, run_audit_claude.sh, Opus 5.5, live). The judge ran from the `r33-20260922c` worktree. Claude Code gave it the checkout's git status, as it does any session in a git repository: +- branch `release-20260922c-child-support-fixed`; +- `reference_audit/2026-09-22/fixes/r33_snap_child_support_treatment.py` listed as modified; +- the latest commit, "Exclude the SNAP child support defect (r33) in release 20260922b (#177)". + +This household is the Michigan child-support case that r33 is about. So the judge was told, outside its prompt, that its own case had been excluded for an engine defect that was then being fixed. + +The judge called no tool. Its verdict (`llm_error / categorical_eligibility`, no reference flag) rests on facts its prompt states: "child support" appears 14 times there. No adjudication record covers the case today. It is still not independent of the decision history beside it, so it is re-judged too. + +Git status also reached three other judge groups; none of them mentions their cases: +- 28 judges on 2026-09-05, whose context listed serving and provenance commits; +- `082 payroll_tax`, whose context listed reference-regeneration commits; +- one verdict that is no longer published. + +The full texts are in `session_contexts.json`. + +### Not an isolated judge, not published + +`us__scenario_020__federal_income_tax_before_refundable_credits` in the unified tree (2026-09-23) was written by the orchestrating main session itself, per its sidecar. The seed re-judged this case, so the release does not publish it. + +### Clean + +In every other bound transcript the judge used no tool at all, or, for workflow judges, made exactly one Read, of its own case's prompt: +- 300 Codex, 131 from 2026-09-05, 17 from 2026-09-22 and 222 from 2026-09-29 in the seed; +- the matching unified rows. + +Every Claude judge, in all three waves, saw the published `prompt.md` byte for byte. + +### Context each wave ran with + +| Wave | Working directory | Tools available | Injected context | +|---|---|---|---| +| Codex | the policybench repo, `--sandbox read-only` | shell (read-only) | Codex's skill list. No run executed a command. | +| 2026-09-05 | `policybench-wt/newmodels` (git worktree, since removed) | all but Bash, Edit, Write, NotebookEdit, WebFetch, WebSearch, Agent, so Read, Grep, Glob, Workflow and ToolSearch were on | `CLAUDE_CODE_SAFE_MODE` suppressed CLAUDE.md. Every one of these judges had the `workflow-authoring` skill (ultracode: "author and run a workflow for every substantive task") injected as a hidden user message, which is how 086 came to launch a workflow. | +| 2026-09-22 and 2026-09-29 workflow judges | a non-git scratch workspace (12 in the `opus55` worktree) | all, with an instruction to read only the case file | Global CLAUDE.md, AGENTS.md and an auto-memory index. The 12 also had PolicyEngine's and policybench's CLAUDE.md as nested memory when they read their prompt. None of the injected text, captured as injected, holds case facts, verdicts or decisions. | + +The hardened runner on `gpt61sol-driver` closes the gaps above: +- It runs each judge from an empty non-git directory with `--tools ""`. +- It denies the file, search, web and shell tools by name. +- It passes `--disable-slash-commands`; its 17 re-judges carry no skill injection. +- It rejects any verdict whose transcript shows a tool call. + +### Not contamination, but worth knowing + +For 230 of the 303 published Codex verdicts, the prompt the judge saw lacks one line of today's `prompt.md`: the definition of `budget_exhausted_at_ceiling`, which was added in `8b2f1956` (2026-08-11). The other 70 match byte for byte, ignoring Codex stderr lines. Whether that line should have changed any label is a separate question, filed as its own task. + +## Fix + +Re-judge five cases with the hardened runner from `gpt61sol-driver` once it is on main: +- `086 federal_refundable_credits`: its judge used tools; +- `045 snap`: its judge saw decision history through git status; +- `014 federal_refundable_credits`, `015 federal_refundable_credits` and `015 head_medicaid_eligible`: their transcripts are missing. + +Each judge runs from an empty directory outside the repo, with tools off, on a subfleet Claude lane's own credentials. The new verdicts replace these cases' row annotations and case notes in a follow-up release on top of `dashboard-data-20260930`. No adjudication record covers any of the five, so no decision changes. If a re-judge flags the reference or leaves `llm_error`, that is a new reference question, and it goes to Max as a decision. + +## Files + +- [`audit_judge_isolation.py`](audit_judge_isolation.py): the classifier, as run on this machine on 2026-09-30. It reads local transcript stores, so it is a record, not a CI check. +- [`verdicts.csv`](verdicts.csv): one row per tree and case. It gives the bound transcript (`~` is the audit machine's home directory), the classification, whether the judge saw exactly the published prompt, and `context_exposure` for 045 snap. +- [`tool_calls.csv`](tool_calls.csv): every tool call. +- [`session_contexts.json`](session_contexts.json): every distinct session context (git status) a Claude judge was shown, with the judges it reached. +- [`codex_prompt_identity.json`](codex_prompt_identity.json): each Codex judge's echoed prompt compared with the published one. +- [`review_redteam.md`](review_redteam.md): the adversarial review. +- [`review_independent.md`](review_independent.md) and [`independent/`](independent/): the independent re-implementation, with its scripts and CSV. + +The fix is release `dashboard-data-20260930b`. [`scripts/finish_rejudge_20260930b.py`](../../scripts/finish_rejudge_20260930b.py) stages it and [`scripts/freeze_rejudge_20260930b.py`](../../scripts/freeze_rejudge_20260930b.py) freezes it. diff --git a/docs/judge_isolation_2026-09-30/audit_judge_isolation.py b/docs/judge_isolation_2026-09-30/audit_judge_isolation.py new file mode 100644 index 00000000..3826aeb1 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/audit_judge_isolation.py @@ -0,0 +1,671 @@ +#!/usr/bin/env python3 +"""Audit whether each published PolicyBench judge verdict came from an isolated judge. + +For every verdict in the audit trees below, bind the verdict to the transcript +that produced it and list every tool the judge called. A binding counts only +when the transcript's structured answer, serialised the way the writers +serialise verdict.json (json.dumps(indent=2, sort_keys=True)), hashes to the +verdict's own sha256. Nothing is inferred: a verdict without a bound transcript +is reported as missing. + +Transcript sources, by judge runner: + scripts/run_audit_codex.sh the case's codex.log (the exec's full stdout) + scripts/run_audit_claude.sh /projects/*/.jsonl, in the + desktop config, each subfleet login's config, + and Logpile's copies + Workflow tool subagents //subagents/workflows// + agent-.jsonl, for every workflow whose + journal names a PolicyBench case + Claude Code main session the orchestrating session's own transcript + +Writes verdicts.csv (one row per tree and case), tool_calls.csv (one row per +judge tool call) and summary.json beside this script. +""" + +from __future__ import annotations + +import collections +import csv +import glob +import hashlib +import json +import os +import re +import sys +from pathlib import Path + +HOME = Path.home() +OUT = Path(__file__).resolve().parent +TREES = { + "seed": Path( + "~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v3/audit/cases" + ), + "unified": Path("~/PolicyEngine/policybench/results/local/unified_audit/audit/cases"), +} +SESSION_ROOTS = [ + HOME / ".claude/projects", + *sorted(Path(p) for p in glob.glob(str(HOME / ".subfleet/logins/*/projects"))), + HOME / "logpile/shared/maxghenis/claudecode", +] +CASE_RE = re.compile(r"us__scenario_\d{3}__[a-z0-9_]+") + + +def canon_sha(obj) -> str: + return hashlib.sha256(json.dumps(obj, indent=2, sort_keys=True).encode("utf-8")).hexdigest() + + +def file_sha(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def wave_of(meta: dict) -> str: + runner = meta.get("judge_runner", "") + day = (meta.get("judged_at_utc") or "")[:10] + if "run_audit_codex" in runner: + # Codex sidecars were backfilled from codex.log; judged_at_utc is the + # backfill time, so the wave is the runner, not a date. + return "codex (sidecars backfilled)" + if day == "2026-09-05": + return "2026-09-05" + if "2026-09-22" <= day <= "2026-09-24": + return "2026-09-22" + if "2026-09-28" <= day <= "2026-09-30": + return "2026-09-29" + return f"other ({day})" + + +def runner_kind(meta: dict) -> str: + runner = meta.get("judge_runner", "") + if "run_audit_codex" in runner: + return "codex" + if "run_audit_claude" in runner: + return "claude_cli" + if runner.startswith("Claude Code Workflow"): + return "workflow_subagent" + if runner.startswith("Claude Code main session"): + return "main_session" + return "unknown" + + +# ---------------------------------------------------------------- transcripts + + +def structured_outputs(path: Path): + """Every StructuredOutput input in a Claude transcript, in order.""" + out = [] + with open(path, encoding="utf-8", errors="replace") as fh: + for line in fh: + if "StructuredOutput" not in line: + continue + try: + event = json.loads(line) + except ValueError: + continue + content = (event.get("message") or {}).get("content") + if not isinstance(content, list): + continue + for part in content: + if ( + isinstance(part, dict) + and part.get("type") == "tool_use" + and part.get("name") == "StructuredOutput" + ): + out.append(part.get("input")) + return out + + +def index_sessions() -> dict[str, list[Path]]: + found: dict[str, list[Path]] = collections.defaultdict(list) + for root in SESSION_ROOTS: + if not root.is_dir(): + continue + for project in os.scandir(root): + if not project.is_dir(): + continue + for entry in os.scandir(project.path): + if entry.name.endswith(".jsonl") and entry.is_file(): + found[entry.name[:-6]].append(Path(entry.path)) + return found + + +def judge_workflow_dirs() -> list[Path]: + dirs = [] + pat = re.compile(rb"us__scenario_\d{3}__") + for journal in glob.glob(str(HOME / ".claude/projects/*/*/subagents/workflows/*/journal.jsonl")): + if pat.search(Path(journal).read_bytes()): + dirs.append(Path(journal).parent) + return sorted(dirs) + + +def index_workflow_agents(dirs: list[Path]): + """sha of each structured answer -> [(agent transcript, workflow, agent id, label)].""" + by_sha: dict[str, list[tuple]] = collections.defaultdict(list) + for wf in dirs: + labels = {} + for line in (wf / "journal.jsonl").read_text(errors="replace").splitlines(): + try: + e = json.loads(line) + except ValueError: + continue + if e.get("type") == "started" and e.get("agentId"): + labels[e["agentId"]] = e.get("label", "") + for agent in sorted(wf.glob("agent-*.jsonl")): + agent_id = agent.stem[len("agent-") :] + for answer in structured_outputs(agent): + if isinstance(answer, dict) and "case_failure_source" in answer: + by_sha[canon_sha(answer)].append((agent, wf.name, agent_id, labels.get(agent_id, ""))) + return by_sha + + +# ---------------------------------------------------------------- classification + +READ_TOOLS = {"Read", "NotebookRead"} +SEARCH_TOOLS = {"Grep", "Glob", "LS"} +SHELL_TOOLS = {"Bash", "BashOutput", "KillShell", "PowerShell"} +BROWSE_TOOLS = {"WebFetch", "WebSearch", "web_search", "web_fetch"} + + +def path_class(path: str, case_id: str) -> str: + p = path or "" + if p.endswith(f"/{case_id}/prompt.md") and "/audit/cases/" in p: + return "own_prompt" + low = p.lower() + if "adjudicat" in low or "decision" in low: + return "adjudication_records" + # Case notes and row annotations are earlier judges' published verdicts. + if any(k in low for k in ("case_notes", "audit_row_annotations", "audit_case_annotations")): + return "prior_verdicts" + if "policyengine-us" in low or "policyengine_us" in low: + return "policyengine_us_checkout" + if "/audit/cases/" in p or low.endswith("verdict.json"): + return "other_audit_cases" + if "/annotations/" in p: + return "annotations" + if "/policybench" in p: + return "policybench_repo_other" + return "other_path" + + +def tool_category(name: str) -> str: + if name in READ_TOOLS: + return "read" + if name in SEARCH_TOOLS: + return "search" + if name in SHELL_TOOLS: + return "shell" + if name in BROWSE_TOOLS or "browser" in name.lower() or "chrome" in name.lower(): + return "browse" + if name == "ToolSearch": + return "tool_search" + if name in {"Agent", "Task"}: + return "subagent" + if name in {"Write", "Edit", "MultiEdit", "NotebookEdit"}: + return "write" + return "other_tool" + + +def result_text(part: dict) -> str: + content = part.get("content") + if isinstance(content, str): + return content + if isinstance(content, list): + return " ".join( + (c.get("text") or "") if isinstance(c, dict) else str(c) for c in content + ) + return "" + + +SKILL_RE = re.compile(r"Base directory for this skill: \S*/skills/([A-Za-z0-9_:.-]+)|/?([A-Za-z0-9_:.-]+)") + + +def message_text(content) -> str: + if isinstance(content, str): + return content + if isinstance(content, list): + return "".join( + (c.get("text") or "") for c in content if isinstance(c, dict) and c.get("type") == "text" + ) + return "" + + +def classify_claude(path: Path, case_id: str, stop_at_sha: str | None = None): + """Tool calls and context of one Claude transcript, up to the bound answer. + + Besides the tool calls it returns what the judge was shown: the working + directory and whether it was a git checkout (the environment snapshot's + isGitRepo, or a gitBranch on any event), every instruction or nested + memory file with the exact content injected, any skill loaded into the + conversation, the first user message (the prompt a `claude -p` judge read + on stdin) and the full text of each Read of its own prompt. + """ + calls, results, attachments = [], {}, collections.Counter() + instructions, cwds, git_repo = [], set(), set() + instruction_contents, skills, full_results = {}, set(), {} + first_user = None + with open(path, encoding="utf-8", errors="replace") as fh: + for line in fh: + try: + event = json.loads(line) + except ValueError: + continue + if event.get("cwd"): + cwds.add(event["cwd"]) + # Claude Code writes gitBranch "HEAD" even outside a repository. + if event.get("gitBranch") not in (None, "", "HEAD"): + git_repo.add(True) + if event.get("type") == "attachment": + att = event.get("attachment") or {} + kind = att.get("type") + attachments[kind] += 1 + if kind == "environment": + snap = att.get("snapshot") or {} + git_repo.add(bool(snap.get("isGitRepo"))) + if snap.get("workingDirectory"): + cwds.add(snap["workingDirectory"]) + if kind in ("instructions", "nested_memory", "memory", "claude_md"): + for f in att.get("files") or [att]: + if isinstance(f, dict): + fpath = f.get("path") or kind + instructions.append(fpath) + body = f.get("content") + if isinstance(body, dict): + body = body.get("content") + if isinstance(body, str): + instruction_contents[fpath] = body + if kind in ("skill", "invoked_skills", "skill_content"): + skills.add(str(att.get("name") or att.get("skillName") or kind)) + msg = event.get("message") or {} + content = msg.get("content") + if event.get("type") == "user" and msg.get("role") == "user": + text = message_text(content) + if first_user is None and text and not event.get("isMeta"): + first_user = text + for m in SKILL_RE.finditer(text): + skills.add(m.group(1) or m.group(2)) + if not isinstance(content, list): + continue + for part in content: + if not isinstance(part, dict): + continue + ptype = str(part.get("type", "")) + if ptype == "tool_result": + full = result_text(part) + results[part.get("tool_use_id")] = (bool(part.get("is_error")), full[:300]) + full_results[part.get("tool_use_id")] = full + elif ptype.endswith("tool_use"): + name = part.get("name") or ptype + if name == "StructuredOutput": + continue + inp = part.get("input") or {} + calls.append({"id": part.get("id"), "name": name, "input": inp}) + rows, own_prompt_reads = [], [] + for c in calls: + name, inp = c["name"], c["input"] + cat = tool_category(name) + if cat == "read" and path_class(inp.get("file_path", ""), case_id) == "own_prompt": + own_prompt_reads.append(full_results.get(c["id"], "")) + target = ( + inp.get("file_path") + or inp.get("path") + or inp.get("pattern") + or inp.get("command") + or inp.get("url") + or inp.get("query") + or "" + ) + pclass = "" + if cat == "read": + pclass = path_class(inp.get("file_path", ""), case_id) + elif cat == "search": + pclass = path_class(inp.get("path", "") or "", case_id) + " | pattern=" + str( + inp.get("pattern", "") + )[:120] + is_err, preview = results.get(c["id"], (None, "")) + rows.append( + { + "tool": name, + "category": cat, + "target": str(target)[:400], + "path_class": pclass, + "result_is_error": is_err, + "result_preview": preview.replace("\n", " ")[:300], + } + ) + return rows, { + "cwds": sorted(cwds), + "cwd_in_git_repo": sorted(git_repo), + "attachments": dict(attachments), + "instructions_files": sorted(set(instructions)), + "instruction_contents": instruction_contents, + "skills": sorted(skills), + "first_user": first_user, + "own_prompt_reads": own_prompt_reads, + } + + +_GIT: dict[str, str] = {} + + +def is_git_checkout(path: str) -> str: + """Whether a judge's working directory is inside a git checkout, checked now.""" + if path not in _GIT: + if not path or not os.path.isdir(path): + _GIT[path] = "gone" + else: + import subprocess + + r = subprocess.run(["git", "-C", path, "rev-parse", "--is-inside-work-tree"], + capture_output=True, text=True) + _GIT[path] = "yes" if r.stdout.strip() == "true" else "no" + return _GIT[path] + + +READ_LINE = re.compile(r"^\s*(\d+)\t(.*)$") + + +def prompt_seen_check(ctx: dict, kind: str, prompt: str, codex_prompt: str | None = None) -> str: + """Did the judge see exactly the published prompt.md? + + claude -p judges read it on stdin, so it is their first user message. + Workflow judges read it with the Read tool, which returns numbered lines + (`Ntext`); every line of the published prompt must come back with + identical text. Codex echoes the prompt after its `user` header. + """ + want = prompt.rstrip("\n") + if kind == "claude_cli": + got = ctx.get("first_user") + if got is None: + return "no prompt in transcript" + return "identical" if got.rstrip("\n") == want else "DIFFERS" + if kind == "workflow_subagent": + reads = ctx.get("own_prompt_reads") or [] + if not reads: + return "no Read of its prompt" + seen = {} + for text in reads: + for line in text.split("\n"): + m = READ_LINE.match(line) + if m: + seen[int(m.group(1))] = m.group(2) + lines = want.split("\n") + missing = [i for i in range(1, len(lines) + 1) if i not in seen] + differ = [i for i in range(1, len(lines) + 1) if i in seen and seen[i] != lines[i - 1]] + extra = [i for i in seen if i > len(lines)] + if not missing and not differ and not extra: + return "identical" + return f"DIFFERS (lines missing {len(missing)}, differing {len(differ)}, extra {len(extra)}; first {(missing + differ + extra)[:5]})" + if kind == "codex": + if codex_prompt is None: + return "no prompt in log" + return "identical" if codex_prompt.strip("\n") == want.strip("\n") else "DIFFERS" + return "" + + +def classify_with_delegates(path: Path, case_id: str): + """A transcript's own calls plus those of any agents it spawned. + + A judge that launches an Agent, Task or Workflow hands work to subagents + whose transcripts live under //subagents/. Their calls + count as the judge's. + """ + rows, ctx = classify_claude(path, case_id) + if any(r["tool"] in {"Agent", "Task", "Workflow"} for r in rows): + sub = path.with_suffix("") / "subagents" + for agent in sorted(sub.rglob("agent-*.jsonl")) if sub.is_dir() else []: + sub_rows, _ = classify_claude(agent, case_id) + for r in sub_rows: + r["tool"] = f"{r['tool']} (delegate {agent.stem})" + rows.extend(sub_rows) + return rows, ctx + + +def codex_final_json(log: Path): + """The final agent message in a codex exec log, parsed as JSON.""" + text = log.read_text(errors="replace") + sections = re.split(r"(?m)^(user|codex|thinking|exec|tokens used)$", text) + headers = collections.Counter(sections[1::2]) + finals = [sections[i + 1] for i in range(1, len(sections), 2) if sections[i] == "codex"] + parsed = None + for body in reversed(finals): + body = body.strip() + try: + parsed = json.loads(body) + break + except ValueError: + m = re.search(r"\{.*\}", body, re.S) + if m: + try: + parsed = json.loads(m.group(0)) + break + except ValueError: + pass + header = dict(re.findall(r"(?m)^(workdir|sandbox|model|session id): (.*)$", text)) + users = [sections[i + 1] for i in range(1, len(sections), 2) if sections[i] == "user"] + header["prompt"] = users[0] if users else None + return parsed, headers, header + + +def classify_codex(log: Path): + """Codex prints each command as an `exec` block; any other tool shows as a header.""" + text = log.read_text(errors="replace") + rows = [] + for m in re.finditer(r"(?m)^exec\n(.*)$", text): + rows.append( + { + "tool": "exec", + "category": "shell", + "target": m.group(1)[:400], + "path_class": "", + "result_is_error": None, + "result_preview": "", + } + ) + odd = sorted( + set(re.findall(r"(?m)^([a-z_ ]+)$", text)) - {"user", "codex", "thinking", "tokens used"} + ) + # Tool lines that carry a colon: `mcp: server/tool started`, `web search: ...`, + # `apply_patch ...`, `patch: ...`. + odd += sorted(set(re.findall(r"(?m)^(mcp:.*|web search:.*|apply_patch.*|patch:.*|tool .*)$", text))) + for h in odd: + rows.append( + { + "tool": h, + "category": "other_tool", + "target": "", + "path_class": "", + "result_is_error": None, + "result_preview": "", + } + ) + return rows + + +# ---------------------------------------------------------------- main + + +def main() -> int: + # Validation runs: --tree name=cases_dir replaces the published trees and + # --out DIR writes the tables elsewhere. + global OUT, TREES + args = sys.argv[1:] + if "--out" in args: + OUT = Path(args[args.index("--out") + 1]) + OUT.mkdir(parents=True, exist_ok=True) + extra = [a.split("=", 1) for a in args if "=" in a and not a.startswith("--")] + if extra: + TREES = {"seed": TREES["seed"], **{k: Path(v) for k, v in extra}} + sessions = index_sessions() + wf_dirs = judge_workflow_dirs() + wf_index = index_workflow_agents(wf_dirs) + print(f"indexed {len(sessions)} session transcripts, {len(wf_dirs)} judge workflows," + f" {sum(len(v) for v in wf_index.values())} workflow structured answers", file=sys.stderr) + + seed_shas = {} + for case in TREES["seed"].iterdir(): + v = case / "verdict.json" + if v.is_file(): + seed_shas[case.name] = file_sha(v) + + verdict_rows, call_rows, injected = [], [], {} + for tree, root in TREES.items(): + for case in sorted(root.iterdir()): + vpath, mpath = case / "verdict.json", case / "verdict.meta.json" + if not vpath.is_file(): + continue + vsha = file_sha(vpath) + # Bind on the canonical serialisation: codex -o keeps the raw bytes. + vcanon = canon_sha(json.loads(vpath.read_text())) + meta = json.loads(mpath.read_text()) if mpath.is_file() else {} + kind = runner_kind(meta) + row = { + "tree": tree, + "case_id": case.name, + "wave": wave_of(meta), + "runner": kind, + "judge_runner": meta.get("judge_runner", ""), + "judged_at_utc": meta.get("judged_at_utc", ""), + "verdict_sha256": vsha, + "sidecar_binds_verdict": meta.get("verdict_sha256") == vsha, + "prompt_sha256_now": file_sha(case / "prompt.md") if (case / "prompt.md").is_file() else "", + "prompt_sha256_sidecar": meta.get("prompt_sha256", ""), + "live_in_published_release": tree == "seed" or seed_shas.get(case.name) == vsha, + "session_id": meta.get("session_id", ""), + "workflow": meta.get("judge_workflow", ""), + "agent_id": meta.get("judge_agent_id", ""), + } + transcript, status, candidates, calls, ctx = "", "missing", 0, [], {} + if kind == "codex": + log = case / "codex.log" + if log.is_file(): + parsed, headers, header = codex_final_json(log) + row["codex_workdir"] = header.get("workdir", "") + row["codex_sandbox"] = header.get("sandbox", "") + if parsed is not None and canon_sha(parsed) == vcanon: + transcript, status, candidates = str(log), "bound", 1 + calls = classify_codex(log) + ctx = {"cwds": [header.get("workdir", "")]} + row["prompt_seen"] = prompt_seen_check( + {}, "codex", (case / "prompt.md").read_text(), header.get("prompt")) + else: + status = "codex.log does not hold this verdict" + else: + status = "missing: no codex.log" + elif kind == "claude_cli": + paths = sessions.get(meta.get("session_id", ""), []) + bound = [p for p in paths if any( + isinstance(a, dict) and canon_sha(a) == vcanon for a in structured_outputs(p))] + candidates = len(paths) + if bound: + # Prefer the live config copy over Logpile's. + bound.sort(key=lambda p: "logpile" in str(p)) + transcript, status = str(bound[0]), "bound" + calls, ctx = classify_with_delegates(bound[0], case.name) + elif paths: + status = "session transcript found but its answer does not hash to this verdict" + else: + status = "missing: no transcript for the session id" + elif kind == "workflow_subagent": + hits = wf_index.get(vcanon, []) + # Only an agent whose journal label names this case binds it. + named = [h for h in hits if case.name in h[3]] or hits + candidates = len(hits) + if named: + agent, wf, agent_id, label = named[0] + transcript, status = str(agent), "bound" + row["workflow"] = row["workflow"] or wf + row["agent_id"] = row["agent_id"] or agent_id + row["bound_label"] = label + calls, ctx = classify_with_delegates(agent, case.name) + row["bound_agents_same_answer"] = len({h[2] for h in hits}) + else: + status = "missing: no workflow agent answer hashes to this verdict" + elif kind == "main_session": + status = "main session judged it itself (not an isolated judge)" + paths = sessions.get(meta.get("session_id", ""), []) + transcript = str(paths[0]) if paths else "" + row["transcript_status"] = status + row["transcript"] = transcript + row["transcript_candidates"] = candidates + row["judge_cwds"] = ";".join(ctx.get("cwds", [])) + row["judge_cwd_in_git_repo"] = ";".join(str(x) for x in ctx.get("cwd_in_git_repo", [])) + row["instructions_files"] = ";".join(ctx.get("instructions_files", [])) + row["attachment_kinds"] = json.dumps(ctx.get("attachments", {}), sort_keys=True) + row["skills_loaded"] = ";".join(ctx.get("skills", [])) + row["judge_cwd_is_git_checkout"] = ";".join( + f"{is_git_checkout(c)}" for c in ctx.get("cwds", [])) + if kind in ("claude_cli", "workflow_subagent") and status == "bound": + row["prompt_seen"] = prompt_seen_check(ctx, kind, (case / "prompt.md").read_text()) + for fpath, body in (ctx.get("instruction_contents") or {}).items(): + injected.setdefault((fpath, hashlib.sha256(body.encode()).hexdigest()), [body, set()])[1].add( + f"{tree}|{row['wave']}") + + cats = collections.Counter() + pclasses = collections.Counter() + for c in calls: + cats[c["category"]] += 1 + if c["path_class"]: + pclasses[c["path_class"].split(" | ")[0]] += 1 + call_rows.append({"tree": tree, "case_id": case.name, "wave": row["wave"], **c}) + read_other = sum(n for k, n in pclasses.items() if k != "own_prompt" and k) + row["n_tool_calls"] = len(calls) + row["read_own_prompt"] = pclasses.get("own_prompt", 0) + row["read_other_files"] = sum( + 1 for c in calls if c["category"] == "read" and c["path_class"] != "own_prompt") + row["read_adjudication_records"] = sum( + 1 for c in calls if c["path_class"].startswith("adjudication_records")) + row["read_policyengine_us"] = sum( + 1 for c in calls if c["path_class"].startswith("policyengine_us_checkout")) + row["searched"] = cats.get("search", 0) + row["shell"] = cats.get("shell", 0) + row["browsed"] = cats.get("browse", 0) + row["other_tools"] = cats.get("other_tool", 0) + cats.get("subagent", 0) + cats.get("write", 0) + row["tool_search"] = cats.get("tool_search", 0) + row["path_classes"] = json.dumps(dict(pclasses), sort_keys=True) + row["affected"] = ( + "yes" if (row["read_other_files"] or row["searched"] or row["shell"] or row["browsed"] + or row["other_tools"] or kind == "main_session") + else ("unknown" if status != "bound" else "no") + ) + _ = read_other + verdict_rows.append(row) + + fields = sorted({k for r in verdict_rows for k in r}, key=lambda k: ( + list(verdict_rows[0].keys()).index(k) if k in verdict_rows[0] else 999, k)) + with open(OUT / "verdicts.csv", "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=fields) + w.writeheader() + w.writerows(verdict_rows) + cfields = ["tree", "case_id", "wave", "tool", "category", "target", "path_class", + "result_is_error", "result_preview"] + with open(OUT / "tool_calls.csv", "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=cfields) + w.writeheader() + w.writerows(call_rows) + + # Every distinct instruction or nested-memory text a judge was shown, as injected. + (OUT / "injected_instructions.json").write_text(json.dumps( + {f"{path}#{sha[:12]}": {"content": body, "seen_in": sorted(seen)} + for (path, sha), (body, seen) in sorted(injected.items())}, indent=1)) + + summary = collections.defaultdict(lambda: collections.Counter()) + for r in verdict_rows: + key = f"{r['tree']} | {r['wave']}" + s = summary[key] + s["verdicts"] += 1 + s[f"transcript:{r['transcript_status'] if r['transcript_status'] == 'bound' else 'not bound'}"] += 1 + s[f"affected:{r['affected']}"] += 1 + for k in ("read_other_files", "read_adjudication_records", "read_policyengine_us", + "searched", "shell", "browsed", "other_tools", "tool_search"): + if r[k]: + s[f"judges_{k}"] += 1 + if r["live_in_published_release"]: + s["live_in_published_release"] += 1 + if r["affected"] == "yes": + s["affected_and_live"] += 1 + (OUT / "summary.json").write_text(json.dumps({k: dict(v) for k, v in sorted(summary.items())}, indent=2)) + print(json.dumps({k: dict(v) for k, v in sorted(summary.items())}, indent=2)) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/docs/judge_isolation_2026-09-30/codex_prompt_identity.json b/docs/judge_isolation_2026-09-30/codex_prompt_identity.json new file mode 100644 index 00000000..04e9c49f --- /dev/null +++ b/docs/judge_isolation_2026-09-30/codex_prompt_identity.json @@ -0,0 +1,2446 @@ +{ + "seed|us__scenario_000__head_medicaid_eligible": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_000__payroll_tax": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_001__payroll_tax": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_001__snap": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_001__spouse_medicare_eligible": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_002__payroll_tax": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_002__spouse_medicaid_eligible": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_003__head_medicare_eligible": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_003__payroll_tax": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_003__state_income_tax_before_refundable_credits": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_004__head_medicaid_eligible": { + "class": "identical", + "other_diff": [] + }, + "seed|us__scenario_004__head_medicare_eligible": { + "class": "identical", + "other_diff": [] + }, + 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added", + "other_diff": [] + }, + "unified|us__scenario_102__federal_refundable_credits": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_102__head_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_102__payroll_tax": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_102__self_employment_tax": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_104__head_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_104__snap": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_107__federal_refundable_credits": { + "class": "only the budget_exhausted_at_ceiling 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definition added", + "other_diff": [] + }, + "unified|us__scenario_109__child2_chip_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_109__child2_head_start_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_109__child2_wic_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_109__child3_chip_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_109__child3_early_head_start_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_109__child3_head_start_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_109__child3_wic_eligible": { + 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[] + }, + "unified|us__scenario_111__federal_income_tax_before_refundable_credits": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_111__head_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_111__payroll_tax": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_111__snap": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_111__spouse_medicaid_eligible": { + "class": "identical", + "other_diff": [] + }, + "unified|us__scenario_112__head_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_112__head_wic_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + 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"class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_117__child3_early_head_start_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_117__child3_head_start_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_117__child3_wic_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_117__head_wic_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_117__spouse_wic_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_118__head_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_118__payroll_tax": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_120__head_medicare_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_121__federal_refundable_credits": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_121__payroll_tax": { + "class": "identical", + "other_diff": [] + }, + "unified|us__scenario_122__head_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_122__payroll_tax": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_123__head_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_123__local_income_tax": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + }, + "unified|us__scenario_123__spouse_medicaid_eligible": { + "class": "only the budget_exhausted_at_ceiling definition added", + "other_diff": [] + } +} \ No newline at end of file diff --git a/docs/judge_isolation_2026-09-30/independent/audit_indep.py b/docs/judge_isolation_2026-09-30/independent/audit_indep.py new file mode 100644 index 00000000..3b2e9ebc --- /dev/null +++ b/docs/judge_isolation_2026-09-30/independent/audit_indep.py @@ -0,0 +1,522 @@ +"""Independent judge-isolation audit of the published PolicyBench verdicts. + +For every verdict in the seed (published 20260929) and unified audit trees: + 1. bind it by content to the transcript that produced it + codex runner -> the case's codex.log final message == verdict (parsed JSON) + Claude judges -> a StructuredOutput tool_use whose input == verdict (parsed JSON), + looked up in the index built by build_index.py + fallback -> a Bash heredoc / Write payload in a main-session transcript == verdict + 2. list every tool call the judge (and any delegate) made, except the StructuredOutput answer + 3. classify: read_other_files / searched / shell / browsed / other_tools -> affected + +Also checks sidecar hashes, sidecar ids vs the bound transcript, and whether the prompt the +judge actually saw equals the published prompt.md. + +usage: python audit_indep.py +Read-only with respect to both audit trees and the repos. +""" +from __future__ import annotations + +import csv +import difflib +import glob +import hashlib +import json +import os +import re +import sys +from collections import Counter, defaultdict +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) +from transcripts import canon, parse_claude, parse_codex, reconstruct_read # noqa: E402 + +TREES = { + "seed": Path("~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v3/audit/cases"), + "unified": Path("~/PolicyEngine/policybench/results/local/unified_audit/audit/cases"), +} +MAIN_SESSIONS = [ + Path("~/.claude/projects/-Users-maxghenis-Library-Application-Support-Claude-scratch-workspaces-ee3763f4-e7ea-4177-bcaf-1362266768c1-4457aa9d-2dc0-4bcf-a128-e50adae6be25-scratch-2026-09-22-c3da5e/3dd3d124-a1eb-44ed-ac89-ce3df9700101.jsonl"), + Path("~/.claude/projects/-Users-maxghenis/3c78d187-d2fd-48e8-bd4e-370d86004afd.jsonl"), +] +HOME = os.path.expanduser("~") + +SEARCH_TOOLS = {"Grep", "Glob", "LS"} +SHELL_TOOLS = {"Bash", "BashOutput", "KillShell", "KillBash", "PowerShell", "Monitor"} +BROWSE_TOOLS = {"WebFetch", "WebSearch", "web_search", "web_fetch"} +READ_TOOLS = {"Read", "NotebookRead"} +DELEGATE_TOOLS = {"Agent", "Task", "Workflow"} + + +def short(p: str) -> str: + return p.replace(HOME, "~") + + +def wave_of(meta: dict) -> str: + runner = meta.get("judge_runner", "") + day = (meta.get("judged_at_utc") or "")[:10] + if runner == "scripts/run_audit_codex.sh": + return "codex" + if day == "2026-09-05": + return "2026-09-05" + if "2026-09-22" <= day <= "2026-09-24": + return "2026-09-22" + if "2026-09-28" <= day <= "2026-09-30": + return "2026-09-29" + return f"unassigned({day})" + + +def classify_call(call: dict, case_id: str) -> str: + name = call["name"] or "" + inp = call.get("input") or {} + if name == "StructuredOutput": + return "structured_output" + if name in READ_TOOLS: + fp = inp.get("file_path") or inp.get("notebook_path") or "" + if fp.endswith(f"/cases/{case_id}/prompt.md"): + return "own_prompt" + return "read_other" + if name in SEARCH_TOOLS: + return "search" + if name in SHELL_TOOLS: + return "shell" + low = name.lower() + if name in BROWSE_TOOLS or low.startswith("mcp__claude-in-chrome") or low.startswith("mcp__chrome") \ + or "browser" in low or "playwright" in low or low.startswith("mcp__computer-use"): + return "browse" + return "other" + + +def describe_call(call: dict) -> str: + inp = call.get("input") or {} + name = call["name"] + if name in READ_TOOLS: + s = f"Read {inp.get('file_path') or inp.get('notebook_path')}" + if inp.get("offset") or inp.get("limit"): + s += f" (offset={inp.get('offset')}, limit={inp.get('limit')})" + return s + if name == "Grep": + return f"Grep pattern={inp.get('pattern')!r} path={inp.get('path')} glob={inp.get('glob')}" + if name == "Glob": + return f"Glob pattern={inp.get('pattern')!r} path={inp.get('path')}" + if name == "LS": + return f"LS {inp.get('path')}" + if name in SHELL_TOOLS: + return f"{name}: {(inp.get('command') or json.dumps(inp))[:400]}" + if name in ("WebFetch", "WebSearch"): + return f"{name} {inp.get('url') or inp.get('query')}" + return f"{name} {json.dumps(inp)[:300]}" + + +# ----------------------------------------------------------------- workflow journals + +_journal_cache: dict[str, dict] = {} + + +def journal_info(agent_path: Path): + """label/phase/key for the agent from its workflow journal, if any.""" + wf_dir = agent_path.parent + j = wf_dir / "journal.jsonl" + if not j.exists(): + return None + key = str(j) + if key not in _journal_cache: + info = {} + results = {} + for ln in open(j, encoding="utf-8"): + ln = ln.strip() + if not ln: + continue + d = json.loads(ln) + if d.get("type") == "started" and d.get("agentId"): + info[d["agentId"]] = {"label": d.get("label"), "phase": d.get("phase"), "key": d.get("key")} + if d.get("type") == "result" and d.get("agentId"): + results[d["agentId"]] = d.get("result") + for aid, r in results.items(): + info.setdefault(aid, {})["result_canon"] = canon(r) if r is not None else None + _journal_cache[key] = info + aid = agent_path.name[len("agent-"):-len(".jsonl")] + rec = dict(_journal_cache[key].get(aid, {})) + rec["workflow_id"] = wf_dir.name + rec["agent_id"] = aid + return rec + + +# ----------------------------------------------------------------- delegates + +def find_delegates(parsed: dict, transcript: Path): + """Transcripts of Agent/Task/Workflow delegates launched by this judge.""" + out = [] + # session dir: ...///subagents/... for subagents, or ...//.jsonl for sessions + p = transcript + if "subagents" in p.parts: + sdir = Path(*p.parts[: p.parts.index("subagents")]) + else: + sdir = p.with_suffix("") + for c in parsed["calls"]: + if c["name"] not in DELEGATE_TOOLS: + continue + res = parsed["results"].get(c["id"], {}).get("text", "") + ids = set(re.findall(r"agentId:\s*([a-z0-9]+)", res)) | set(re.findall(r"agent-([a-z0-9]+)", res)) + wfs = set(re.findall(r"(wf_[0-9a-f]{8}-[0-9a-f]{3})", res)) + found = [] + for aid in ids: + found += glob.glob(str(sdir / "subagents" / "**" / f"agent-{aid}.jsonl"), recursive=True) + for wf in wfs: + found += glob.glob(str(sdir / "subagents" / "workflows" / wf / "agent-*.jsonl")) + out.append({"call": c["id"], "tool": c["name"], "agent_ids": sorted(ids), "workflows": sorted(wfs), + "transcripts": sorted(set(found))}) + return out + + +# ----------------------------------------------------------------- prompt fidelity + +def compare_text(seen: str, published: str) -> tuple[str, list[str]]: + a = seen.strip().split("\n") + b = published.strip().split("\n") + if a == b: + return "identical", [] + diff = [ln for ln in difflib.unified_diff(a, b, lineterm="", n=0) if not ln.startswith(("---", "+++", "@@"))] + return f"differs({sum(1 for d in diff if d.startswith('-'))} lines only-seen, {sum(1 for d in diff if d.startswith('+'))} lines only-published)", diff[:12] + + +def compare_read(lines_seen: dict, published: str): + pub = published.split("\n") + if pub and pub[-1] == "": + pub = pub[:-1] + n = len(pub) + covered = [i for i in range(1, n + 1) if i in lines_seen] + mism, trunc = [], 0 + for i in covered: + s, p = lines_seen[i], pub[i - 1] + if s == p: + continue + # Read truncates very long lines; accept a strict prefix + if len(s) < len(p) and p.startswith(s.rstrip(".… ")[: max(0, len(s) - 40)]): + trunc += 1 + continue + mism.append(i) + extra = [i for i in lines_seen if i > n] + if not covered: + return "no-content-returned", [] + status = "identical" + if len(covered) < n: + status = f"partial-read({len(covered)}/{n} lines)" + if trunc: + status += f"+{trunc}-truncated-lines-prefix-match" + if mism or extra: + status = f"differs({len(mism)} changed, {len(extra)} extra lines)" + detail = [f"L{i}: seen={lines_seen[i][:160]!r} published={pub[i-1][:160]!r}" for i in mism[:4]] + return status, detail + + +# ----------------------------------------------------------------- main + +def main(): + index = json.load(open(sys.argv[1]))["index"] + out_dir = Path(sys.argv[2]) + out_dir.mkdir(parents=True, exist_ok=True) + + verdicts = [] + for tree, root in TREES.items(): + for d in sorted(p for p in root.iterdir() if p.is_dir()): + vb = (d / "verdict.json").read_bytes() + verdicts.append({ + "tree": tree, "case_id": d.name, "dir": d, + "vbytes": vb, "v": json.loads(vb), + "meta": json.loads((d / "verdict.meta.json").read_text()), + "prompt": (d / "prompt.md").read_text(encoding="utf-8"), + "files": sorted(os.listdir(d)), + }) + seed_bytes = {r["case_id"]: r["vbytes"] for r in verdicts if r["tree"] == "seed"} + + main_parsed = {} + rows, details = [], [] + for r in verdicts: + meta, v, case_id = r["meta"], r["v"], r["case_id"] + wave = wave_of(meta) + runner = meta.get("judge_runner", "") + notes, anomalies = [], [] + rec = { + "tree": r["tree"], "case_id": case_id, "wave": wave, "runner": runner, + "transcript_path": "", "binding_method": "", "n_tool_calls": "", + "read_other_files": "", "searched": "", "shell": "", "browsed": "", "other_tools": "", + "affected": "unknown", + "live_in_published_release": "yes" if r["tree"] == "seed" else ( + "yes" if seed_bytes.get(case_id) == r["vbytes"] else "no"), + } + det = {"tree": r["tree"], "case_id": case_id, "wave": wave, "runner": runner, "meta": meta, + "calls": [], "delegates": [], "anomalies": anomalies} + + # sidecar integrity + vsha = hashlib.sha256(r["vbytes"]).hexdigest() + if meta.get("verdict_sha256") != vsha: + anomalies.append(f"sidecar verdict_sha256 {meta.get('verdict_sha256')} != sha256(verdict.json) {vsha}") + canonical_bytes = json.dumps(v, indent=2, sort_keys=True).encode() + # codex -o keeps the raw final message, so only Claude-written verdicts must be canonical + if canonical_bytes != r["vbytes"] and runner != "scripts/run_audit_codex.sh": + anomalies.append("verdict.json bytes are not json.dumps(v, indent=2, sort_keys=True)") + psha = hashlib.sha256(r["prompt"].encode("utf-8")).hexdigest() + if meta.get("prompt_sha256") and meta["prompt_sha256"] != psha: + anomalies.append(f"sidecar prompt_sha256 != sha256(published prompt.md)") + if "verdict.json.tmp" in r["files"]: + anomalies.append("stray verdict.json.tmp in case dir") + tmpb = (r["dir"] / "verdict.json.tmp").read_bytes() + try: + tv = json.loads(tmpb) + det["tmp_equals_verdict"] = tv == v + if tv != v: + anomalies.append("verdict.json.tmp differs from verdict.json") + except Exception: + anomalies.append("verdict.json.tmp unparseable") + + calls = [] + bound = False + if runner == "scripts/run_audit_codex.sh": + log = r["dir"] / "codex.log" + if not log.exists(): + rec["binding_method"] = "missing" + notes.append("no codex.log in case dir") + else: + cx = parse_codex(log) + det["codex"] = {k: cx[k] for k in ("header", "blocks", "execs", "tool_markers", "stderr_noise_in_prompt")} + if cx["final_obj"] == v: + bound = True + rec["transcript_path"] = str(log) + rec["binding_method"] = "codex.log final message (after 'tokens used') == verdict.json as parsed JSON" + if cx["last_msg_obj"] is not None and cx["last_msg_obj"] != v: + anomalies.append("codex.log 'codex' block message differs from final stdout message") + for e in cx["execs"]: + calls.append({"name": "exec", "input": {"command": e}, "cls": "shell"}) + for m in cx["tool_markers"]: + calls.append({"name": "codex-marker", "input": {"line": m}, "cls": "other"}) + extra_blocks = [b for b in cx["blocks"] if b not in ("user", "codex", "tokens used", "exec")] + if extra_blocks: + anomalies.append(f"codex.log has unexpected blocks {extra_blocks}") + if cx["blocks"] != ["user", "codex", "tokens used"]: + notes.append(f"codex blocks: {cx['blocks']}") + st, diff = compare_text(cx["prompt"] or "", r["prompt"]) + det["prompt_fidelity"] = st + det["prompt_diff"] = diff + wd = cx["header"].get("workdir") + det["workdir"] = wd + notes.append(f"workdir={wd}; sandbox={cx['header'].get('sandbox')}; model={cx['header'].get('model')}") + if st != "identical": + anomalies.append(f"prompt in codex.log vs published prompt.md: {st}") + else: + rec["binding_method"] = "missing" + notes.append( + "case codex.log holds a different run's answer (its final message != verdict.json; " + f"log session {cx['header'].get('session id')}); no other codex.log copy under " + "policybench*/results/**/audit/cases holds this verdict; codex ran --ephemeral so no rollout; " + "Aug 17-18 rollouts in ~/.subfleet/lanes/codex-*/sessions and Logpile codex hold no audit prompt") + else: + hits = [h for h in index.get(canon(v), []) if "/logpile/" not in h["path"]] + lp = [h for h in index.get(canon(v), []) if "/logpile/" in h["path"]] + paths = sorted({h["path"] for h in hits}) + if len(paths) == 1: + bound = True + tpath = Path(paths[0]) + h = hits[0] + rec["transcript_path"] = str(tpath) + rec["binding_method"] = ("StructuredOutput tool_use input == verdict.json as parsed JSON " + "(unique match among all indexed Claude transcripts)") + if lp: + notes.append(f"Logpile copy also holds it: {short(lp[0]['path'])}") + parsed = parse_claude(tpath) + det["transcript"] = {k: parsed[k] for k in ("session_ids", "agent_ids", "cwds", "models", "first_ts", "last_ts", "n_rows", "sidechain_rows")} + det["injected_attachments"] = Counter(parsed["injected"]) + # context the harness put in front of the judge besides its prompt (not tool calls) + ctx = [] + for row in __import__("transcripts").load_lines(tpath): + a = row.get("attachment") if row.get("type") == "attachment" else None + if a and a.get("type") == "nested_memory": + ctx.append(f"nested_memory {short(a.get('path', ''))}") + if a and a.get("type") == "session_context" and (a.get("context") or {}).get("gitStatus"): + gs = a["context"]["gitStatus"] + br = re.search(r"Current branch: (\S+)", gs) + commits = re.findall(r"^[0-9a-f]{7,} (.+)$", gs, re.M) + ctx.append(f"gitStatus of {parsed['cwds'][0] if parsed['cwds'] else '?'} (branch " + f"{br.group(1) if br else '?'}; recent commits: {' / '.join(c_[:70] for c_ in commits[:3])})") + if any("workflow-authoring" in s_ for _, s_ in parsed["user_strings"]): + ctx.append("workflow-authoring skill text injected as a user turn") + relay = [s_ for _, s_ in parsed["user_strings"] if s_.startswith("[Workflow harness — user request]")] + if relay: + rq = relay[0].split("this request wins:", 1)[-1].strip() + ctx.append(f"relayed parent user request: {rq[:120]!r}") + if parsed["injected"].count("instructions"): + ctx.append("CLAUDE.md instructions attachment") + det["context_injections"] = ctx + # identity checks + jinfo = journal_info(tpath) if "workflows" in tpath.parts else None + if jinfo: + det["journal"] = jinfo + lab = jinfo.get("label") or "" + notes.append(f"workflow {jinfo['workflow_id']} agent {jinfo['agent_id']} label {lab}") + if lab.split("#")[0].removeprefix("judge:") != case_id: + anomalies.append(f"journal label {lab!r} is not this case") + if jinfo.get("result_canon") != canon(v): + anomalies.append("journal result for this agent != verdict") + if meta.get("judge_agent_id") and meta["judge_agent_id"] != jinfo["agent_id"]: + anomalies.append(f"sidecar judge_agent_id {meta['judge_agent_id']} != bound agent {jinfo['agent_id']}") + if meta.get("judge_workflow") and meta["judge_workflow"] != jinfo["workflow_id"]: + anomalies.append(f"sidecar judge_workflow {meta['judge_workflow']} != bound workflow {jinfo['workflow_id']}") + sid = meta.get("session_id") + tsid = parsed["session_ids"][0] if parsed["session_ids"] else None + if sid and sid.startswith("wf_"): + if sid != jinfo["workflow_id"]: + anomalies.append(f"sidecar session_id (a workflow id) {sid} != bound workflow {jinfo['workflow_id']}") + else: + notes.append(f"sidecar session_id is the workflow id; parent session {tsid}") + elif sid and sid != tsid: + anomalies.append(f"sidecar session_id {sid} != transcript session {tsid}") + else: + sid = meta.get("session_id") + if sid and sid not in parsed["session_ids"]: + anomalies.append(f"sidecar session_id {sid} != transcript session {parsed['session_ids']}") + if tpath.stem != sid: + anomalies.append(f"transcript filename {tpath.stem} != sidecar session id {sid}") + notes.append(f"session {parsed['session_ids']} cwd={parsed['cwds']}") + sos = [c for c in parsed["calls"] if c["name"] == "StructuredOutput"] + matching = [c for c in sos if canon(c["input"]) == canon(v)] + if len(sos) > 1: + notes.append(f"{len(sos)} StructuredOutput calls ({len(matching)} equal to the verdict)") + if parsed["calls"] and parsed["calls"][-1]["name"] != "StructuredOutput": + anomalies.append("last tool call is not StructuredOutput") + if sos and matching and sos[-1] is not matching[-1]: + anomalies.append("verdict matches a non-final StructuredOutput call") + answer_id = matching[-1]["id"] if matching else None + for c in parsed["calls"]: + if c["id"] == answer_id: + continue + cc = dict(c) + cc["cls"] = classify_call(c, case_id) + res = parsed["results"].get(c["id"]) + cc["is_error"] = bool(res and res["is_error"]) + cc["result_head"] = (res["text"][:200] if res else None) + calls.append(cc) + # delegates + dels = find_delegates(parsed, tpath) + det["delegates"] = dels + for dl in dels: + for dt in dl["transcripts"]: + dp = parse_claude(Path(dt)) + for c in dp["calls"]: + cc = dict(c) + cc["cls"] = classify_call(c, case_id) + cc["delegate"] = dt + calls.append(cc) + if not dl["transcripts"]: + anomalies.append(f"delegate {dl['tool']} launched but its transcript was not found") + # prompt fidelity + own_reads = [c for c in calls if c.get("cls") == "own_prompt"] + if own_reads: + texts = [parsed["results"].get(c["id"], {}).get("text", "") for c in own_reads] + lines_seen = reconstruct_read(texts) + st, dd = compare_read(lines_seen, r["prompt"]) + det["prompt_fidelity"] = st + det["prompt_diff"] = dd + roots = sorted({str(Path(c["input"]["file_path"]).parent.parent.parent) for c in own_reads}) + det["prompt_read_from"] = roots + if str(TREES[r["tree"]].parent) not in roots: + notes.append(f"judge read its prompt from {', '.join(short(x) for x in roots)} (not this tree)") + if not st.startswith("identical"): + anomalies.append(f"prompt the judge read vs published prompt.md: {st}") + # also the on-disk copy it read, today + for x in roots: + fp = Path(x) / "cases" / case_id / "prompt.md" + if fp.exists() and fp.read_bytes() != r["prompt"].encode("utf-8"): + det.setdefault("disk_prompt_differs", []).append(str(fp)) + else: + # prompt arrived as the first user message (claude -p < prompt.md) or in the task text + first = parsed["user_strings"][0][1] if parsed["user_strings"] else "" + if "Workflow harness" in first or any("Workflow harness" in s for _, s in parsed["user_strings"][:2]): + det["prompt_fidelity"] = "prompt-not-read" + anomalies.append("workflow judge never read its prompt.md") + else: + st, dd = compare_text(first, r["prompt"]) + det["prompt_fidelity"] = st + det["prompt_diff"] = dd + if st != "identical": + anomalies.append(f"first user message vs published prompt.md: {st}") + extra_user = [s[:120] for _, s in parsed["user_strings"][1:]] + if extra_user: + det["extra_user_messages"] = extra_user + if len(sos) == 0: + anomalies.append("no StructuredOutput in bound transcript (?)") + elif len(paths) > 1: + rec["binding_method"] = "ambiguous" + notes.append(f"verdict equals StructuredOutput in {len(paths)} transcripts: " + "; ".join(short(p) for p in paths)) + else: + # fallback: a main session wrote it with Bash/Write + found = None + needle = (v.get("rationale") or "")[:80] + for ms in MAIN_SESSIONS: + if not needle: + break + if ms not in main_parsed: + main_parsed[ms] = parse_claude(ms) + mp = main_parsed[ms] + for c in mp["calls"]: + blob = json.dumps(c["input"], ensure_ascii=False) + if needle not in blob and json.dumps(needle)[1:-1] not in blob: + continue + text = (c["input"].get("command") or c["input"].get("content") or "") + for m in re.finditer(r"<<'?EOF'?\n(.*?)\nEOF", text, re.S): + try: + if json.loads(m.group(1)) == v: + found = (ms, c) + except Exception: + pass + if c["name"] == "Write": + try: + if json.loads(text) == v: + found = (ms, c) + except Exception: + pass + if found: + ms, c = found + bound = True + rec["transcript_path"] = str(ms) + rec["binding_method"] = (f"{c['name']} tool_use in the main session writes a verdict.json whose content == " + "verdict.json as parsed JSON (no StructuredOutput: the main session judged it itself)") + det["main_session_answer_call"] = {"id": c["id"], "row": c["row"], "ts": c["ts"]} + else: + rec["binding_method"] = "missing" + notes.append("no indexed StructuredOutput and no main-session write equals this verdict") + + if bound and not det.get("main_session_answer_call"): + det["calls"] = [{k: c.get(k) for k in ("name", "cls", "input", "is_error", "delegate", "ts")} for c in calls] + cls = Counter(c["cls"] for c in calls) + rec["n_tool_calls"] = len(calls) + rec["read_other_files"] = "yes" if cls["read_other"] else "no" + rec["searched"] = "yes" if cls["search"] else "no" + rec["shell"] = "yes" if cls["shell"] else "no" + rec["browsed"] = "yes" if cls["browse"] else "no" + rec["other_tools"] = "yes" if cls["other"] else "no" + rec["affected"] = "yes" if any(cls[k] for k in ("read_other", "search", "shell", "browse", "other")) else "no" + touched = [describe_call(c) + (" [error]" if c.get("is_error") else "") for c in calls if c["cls"] != "own_prompt"] + det["touched"] = touched + own = cls["own_prompt"] + if own: + notes.append(f"{own} Read(s) of its own case prompt.md") + if touched: + notes.append("TOUCHED: " + " | ".join(touched[:12]) + (f" | (+{len(touched)-12} more)" if len(touched) > 12 else "")) + rec["notes"] = "; ".join(notes + [f"ANOMALY: {a}" for a in anomalies]) + rows.append(rec) + details.append(det) + + # main-session judged verdicts get their segment analysed separately (see main_session_segment.py) + with open(out_dir / "verdicts_indep_auto.csv", "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=list(rows[0].keys())) + w.writeheader() + w.writerows(rows) + json.dump(details, open(out_dir / "details_auto.json", "w"), indent=1, default=str) + c = Counter((r["tree"], r["wave"], r["binding_method"].split(" ")[0], r["affected"]) for r in rows) + for k, n in sorted(c.items()): + print(n, k) + + +if __name__ == "__main__": + main() diff --git a/docs/judge_isolation_2026-09-30/independent/build_index.py b/docs/judge_isolation_2026-09-30/independent/build_index.py new file mode 100644 index 00000000..fb76f594 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/independent/build_index.py @@ -0,0 +1,87 @@ +"""Index every StructuredOutput answer in the candidate Claude transcripts. + +Output: a JSON map canonical-verdict -> list of {path, agent_ids, session_ids, row, ts}. +Candidate transcripts: + * each sidecar session id's .jsonl in ~/.claude/projects/*, ~/.subfleet/logins/*/projects/*, + and the Logpile copy (~/logpile/shared/maxghenis/claudecode/*) + * every agent-*.jsonl under /subagents/ (recursively) for every session that owns a + subagents dir among those, plus the two Workflow parent sessions found by glob. +Files are prefiltered on the bytes '"StructuredOutput"' before JSON parsing. + +usage: python build_index.py [extra transcript paths or dirs ...] +""" +from __future__ import annotations + +import json +import os +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +from transcripts import canon, load_lines # noqa: E402 + +TOOL_TYPES = {"tool_use", "server_tool_use", "mcp_tool_use"} + + +def so_entries(path: Path): + b = open(path, "rb").read() + if b'"StructuredOutput"' not in b: + return [] + out = [] + seen = set() + for i, d in enumerate(load_lines(path)): + msg = d.get("message") if isinstance(d.get("message"), dict) else None + if not msg or d.get("type") != "assistant" or not isinstance(msg.get("content"), list): + continue + for p in msg["content"]: + if isinstance(p, dict) and p.get("type") in TOOL_TYPES and p.get("name") == "StructuredOutput": + if p.get("id") in seen: + continue + seen.add(p.get("id")) + out.append({ + "canon": canon(p.get("input")), + "row": i, + "ts": d.get("timestamp"), + "agent_id": d.get("agentId"), + "session_id": d.get("sessionId"), + "tool_use_id": p.get("id"), + }) + return out + + +def main(): + hits = json.load(open(sys.argv[1])) + out_path = Path(sys.argv[2]) + files = set() + session_dirs = set() + for k, v in hits.items(): + for p in v: + if k.endswith("/dir"): + session_dirs.add(p) + else: + files.add(p) + d = p[: -len(".jsonl")] + if os.path.isdir(d): + session_dirs.add(d) + for extra in sys.argv[3:]: + if os.path.isdir(extra): + session_dirs.add(extra) + else: + files.add(extra) + for sd in sorted(session_dirs): + sub = Path(sd) / "subagents" + if sub.is_dir(): + for p in sub.rglob("agent-*.jsonl"): + files.add(str(p)) + index = {} + n_so = 0 + for f in sorted(files): + for e in so_entries(Path(f)): + n_so += 1 + index.setdefault(e.pop("canon"), []).append({"path": f, **e}) + json.dump({"files_scanned": sorted(files), "index": index}, open(out_path, "w")) + print(f"scanned {len(files)} files, {n_so} StructuredOutput calls, {len(index)} distinct answers") + + +if __name__ == "__main__": + main() diff --git a/docs/judge_isolation_2026-09-30/independent/crosscheck_walk.py b/docs/judge_isolation_2026-09-30/independent/crosscheck_walk.py new file mode 100644 index 00000000..027f5b7f --- /dev/null +++ b/docs/judge_isolation_2026-09-30/independent/crosscheck_walk.py @@ -0,0 +1,47 @@ +"""Cross-check: recursively walk every row of each bound Claude transcript for tool_use parts +anywhere in the JSON (not only assistant message content) and compare with the audit's call lists. +usage: python crosscheck_walk.py verdicts_indep.csv verdicts_indep_details.json""" +import csv, json, sys, collections + +def walk(o, out): + if isinstance(o, dict): + if o.get("type") in ("tool_use", "server_tool_use", "mcp_tool_use") and "name" in o: + out.append((o.get("id"), o["name"])) + for v in o.values(): + walk(v, out) + elif isinstance(o, list): + for v in o: + walk(v, out) + +rows = list(csv.DictReader(open(sys.argv[1]))) +det = {(d["tree"], d["case_id"]): d for d in json.load(open(sys.argv[2]))} +cache = {} +res = collections.Counter(); bad = [] +for r in rows: + p = r["transcript_path"] + if not p.endswith(".jsonl") or "3dd3d124-a1eb-44ed-ac89-ce3df9700101.jsonl" in p: + continue + if p not in cache: + found = [] + for ln in open(p, "rb"): + ln = ln.strip() + if ln: + walk(json.loads(ln), found) + ids = {} + for i, n in found: + ids.setdefault(i, n) + cache[p] = ids + ids = cache[p] + d = det[(r["tree"], r["case_id"])] + judge_calls = [c for c in d["calls"] if not c.get("delegate")] + names_walk = collections.Counter(n for n in ids.values()) + # audit excludes exactly one answer StructuredOutput from the judge's own file + names_audit = collections.Counter(c["name"] for c in judge_calls) + names_audit["StructuredOutput"] += 1 + ok = names_walk == names_audit + res[(r["wave"], ok)] += 1 + if not ok: + bad.append((r["tree"], r["case_id"], dict(names_walk), dict(names_audit))) +print(res) +for b in bad[:10]: + print(b) diff --git a/docs/judge_isolation_2026-09-30/independent/finalize.py b/docs/judge_isolation_2026-09-30/independent/finalize.py new file mode 100644 index 00000000..a406a97d --- /dev/null +++ b/docs/judge_isolation_2026-09-30/independent/finalize.py @@ -0,0 +1,165 @@ +"""Finish the independent audit: score the main-session judge, write the final CSV and summaries. + +The one verdict a main session wrote itself (no StructuredOutput) is bound by audit_indep.py to +the Bash heredoc that wrote it. Its "judge" is that session; this script lists the tool calls in +the judging segment (from the first read of the case prompt for judging to the verdict write), +classifies them, and records the earlier case-specific calls still in the session's context. + +usage: python finalize.py +""" +from __future__ import annotations + +import csv +import json +import re +import sys +from collections import Counter, defaultdict +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) +from transcripts import parse_claude # noqa: E402 + +MAIN = Path("~/.claude/projects/-Users-maxghenis-Library-Application-Support-Claude-scratch-workspaces-ee3763f4-e7ea-4177-bcaf-1362266768c1-4457aa9d-2dc0-4bcf-a128-e50adae6be25-scratch-2026-09-22-c3da5e/3dd3d124-a1eb-44ed-ac89-ce3df9700101.jsonl") +MAIN_CASE = ("unified", "us__scenario_020__federal_income_tax_before_refundable_credits") +PROMPT = "unified_audit/audit/cases/us__scenario_020__federal_income_tax_before_refundable_credits/prompt.md" + + +def main_session_row(rec: dict, det: dict): + ans = det["main_session_answer_call"] + parsed = parse_claude(MAIN) + calls = parsed["calls"] + ans_i = next(i for i, c in enumerate(calls) if c["id"] == ans["id"]) + # segment start: the first call after the answer's predecessors that reads the case prompt to judge it + start_i = ans_i + while start_i > 0: + c = calls[start_i - 1] + cmd = (c["input"] or {}).get("command", "") if isinstance(c["input"], dict) else "" + desc = (c["input"] or {}).get("description", "") if isinstance(c["input"], dict) else "" + start_i -= 1 + if PROMPT in cmd and "to judge it" in desc: + break + seg = calls[start_i:ans_i + 1] + prior = [c for c in calls[:start_i] + if "scenario_020" in json.dumps(c["input"]) or "pb-judge-020" in json.dumps(c["input"])] + touched = [] + for c in seg: + inp = c["input"] or {} + touched.append({"row": c["row"], "ts": c["ts"], "name": c["name"], + "description": inp.get("description"), "command": inp.get("command", "")[:600], + "is_answer_write": c["id"] == ans["id"]}) + det["calls"] = touched + det["prior_case_calls_in_context"] = [ + {"row": c["row"], "ts": c["ts"], "name": c["name"], "description": (c["input"] or {}).get("description"), + "command": (c["input"] or {}).get("command", "")[:300]} for c in prior] + names = Counter(c["name"] for c in seg) + assert set(names) == {"Bash"}, names + rec["n_tool_calls"] = len(seg) + rec["read_other_files"] = "yes" + rec["searched"] = "no" + rec["shell"] = "yes" + rec["browsed"] = "no" + rec["other_tools"] = "no" + rec["affected"] = "yes" + rec["notes"] = ( + f"Judge = orchestrating main session 3dd3d124 (no StructuredOutput). Judging segment = {len(seg)} Bash calls, " + f"rows {seg[0]['row']}-{seg[-1]['row']} ({seg[0]['ts']} to {seg[-1]['ts']}): read its prompt with sed/awk; " + "ran PolicyEngine on the case (triage/pe_case.py scenario_020 ... --calc adjusted_gross_income taxable_income " + "salt_deduction net_investment_income_tax ...); rewrote the case's reference explanation in " + "adds202609/reference_v12/us_case_reference_explanations.csv and the opus55 worktree's annotations CSV; read the " + "previous verdict for this case from adds202609/verdicts-board42-b.json ('Read the previous 020 verdict to adapt'); " + "then wrote verdict.json by heredoc and ran scripts/validate_verdict.py on it. " + f"Earlier in the same context: {len(prior)} case-020 calls, incl. regen_references.py (grep scenario_020), " + "restoring 020's reference explanation from _pre_conventions, and two aborted Subfleet judge lanes (pb-judge-020, 020b). " + "Session context also held the repo work of the whole session (compaction at row 3211)." + ) + return rec, det + + +def main(): + auto, out = Path(sys.argv[1]), Path(sys.argv[2]) + rows = list(csv.DictReader(open(auto / "verdicts_indep_auto.csv"))) + details = json.load(open(auto / "details_auto.json")) + dmap = {(d["tree"], d["case_id"]): d for d in details} + final = [] + for r in rows: + d = dmap[(r["tree"], r["case_id"])] + if (r["tree"], r["case_id"]) == MAIN_CASE: + r, d = main_session_row(r, d) + dmap[(r["tree"], r["case_id"])] = d + if r["runner"] == "scripts/run_audit_codex.sh" and d["meta"].get("judged_at_utc", "").startswith("2026-08-18T01:27:48"): + r["notes"] += ("; sidecar judged_at_utc is the 2026-08-18T01:27:48Z restore time: session 3c78d187 (line 19580) " + "copied this verdict.json back from Time Machine snapshot 2026-08-17-194721 after #151's prompt " + "change invalidated banked verdicts; the verdict was judged before 19:47 local 2026-08-17") + if r["binding_method"] == "missing" and r["runner"] == "scripts/run_audit_codex.sh": + r["notes"] += ("; WHY MISSING: at 2026-08-18T01:25:58Z session 3c78d187 (~/.claude/projects/-Users-maxghenis/" + "3c78d187-d2fd-48e8-bd4e-370d86004afd.jsonl line 19542) ran pkill -f run_audit_codex, killing an in-flight " + "re-judge of this case that had already overwritten codex.log; at 01:27:48Z (line 19580) it restored " + "verdict.json from Time Machine snapshot 2026-08-17-194721. The published verdict's own codex.log " + "existed only in that snapshot; today the only local snapshot is 2026-09-30 and /Volumes/TimeMachine " + "is not readable from this session. The sidecar's backfilled_from=codex.log and model point at the " + "killed re-run's log (its answer, which the killed run never published, shows no exec block)") + if (Path("~/PolicyEngine/policybench/results/local/unified_audit/audit/cases") / r["case_id"] / "verdict.json.tmp").exists(): + r["notes"] += "; the killed re-run's -o output survives as unified//verdict.json.tmp (equal to codex.log's answer)" + if (r["tree"], r["case_id"]) in (("seed", "us__scenario_086__federal_refundable_credits"), + ("unified", "us__scenario_086__federal_refundable_credits")): + r["notes"] = ("LEAK DETAIL: Grep content output (not only Read) put prior-audit text in front of the delegates: " + "annotations/.../us_audit_row_annotations.csv rows 5559 (scenario_086 federal_income_tax row) and 5581 " + "(scenario_086 federal_refundable_credits claude-opus-4.7 diagnosis), us_case_notes.csv, " + "us_case_reference_explanations.csv, paper/snapshot/20260501/runs/.../reference_outputs.csv and data.json; " + "the TaskOutput result the judge read cites us_audit_row_annotations.csv line 5559 and PolicyEngine's " + "trace. Runner --disallowedTools blocked Bash/Edit/Write/NotebookEdit/WebFetch/WebSearch/Agent only, so " + "Workflow, ToolSearch, TaskOutput, Read, Grep, Glob stayed available; delegates' ToolSearch select:Bash " + "found nothing. " + r["notes"]) + ctx = d.get("context_injections") or [] + if ctx: + r["notes"] += "; CONTEXT (not a tool call): " + ", ".join(sorted(set(ctx))) + final.append(r) + + fields = ["tree", "case_id", "wave", "runner", "transcript_path", "binding_method", "n_tool_calls", + "read_other_files", "searched", "shell", "browsed", "other_tools", "affected", + "live_in_published_release", "notes"] + with open(out / "verdicts_indep.csv", "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=fields) + w.writeheader() + for r in final: + w.writerow({k: r[k] for k in fields}) + json.dump([dmap[(r["tree"], r["case_id"])] for r in final], open(out / "verdicts_indep_details.json", "w"), + indent=1, default=str) + + # summaries + tab = defaultdict(Counter) + for r in final: + k = (r["tree"], r["wave"]) + tab[k]["verdicts"] += 1 + tab[k]["bound"] += r["binding_method"] not in ("missing", "ambiguous") + tab[k]["missing"] += r["binding_method"] == "missing" + tab[k]["affected"] += r["affected"] == "yes" + tab[k]["unknown"] += r["affected"] == "unknown" + tab[k]["live"] += r["live_in_published_release"] == "yes" + tab[k]["live_affected"] += r["live_in_published_release"] == "yes" and r["affected"] == "yes" + tab[k]["live_missing"] += r["live_in_published_release"] == "yes" and r["binding_method"] == "missing" + lines = ["| tree | wave | verdicts | bound | missing | affected | live | live & affected | live & missing |", + "|---|---|---|---|---|---|---|---|---|"] + order = ["codex", "2026-09-05", "2026-09-22", "2026-09-29"] + for tree in ("seed", "unified"): + for wv in order: + c = tab.get((tree, wv)) + if not c: + lines.append(f"| {tree} | {wv} | 0 | 0 | 0 | 0 | 0 | 0 | 0 |") + continue + lines.append(f"| {tree} | {wv} | {c['verdicts']} | {c['bound']} | {c['missing']} | {c['affected']} | " + f"{c['live']} | {c['live_affected']} | {c['live_missing']} |") + (out / "summary_table.md").write_text("\n".join(lines) + "\n") + print("\n".join(lines)) + anomalies = Counter() + for d in dmap.values(): + for a in d.get("anomalies", []): + anomalies[(d["tree"], d["wave"], re.sub(r"\d+", "#", a)[:140])] += 1 + print() + for k, v in sorted(anomalies.items()): + print(v, k) + + +if __name__ == "__main__": + main() diff --git a/docs/judge_isolation_2026-09-30/independent/summary_table.md b/docs/judge_isolation_2026-09-30/independent/summary_table.md new file mode 100644 index 00000000..d7883809 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/independent/summary_table.md @@ -0,0 +1,10 @@ +| tree | wave | verdicts | bound | missing | affected | live | live & affected | live & missing | +|---|---|---|---|---|---|---|---|---| +| seed | codex | 303 | 300 | 3 | 0 | 303 | 0 | 3 | +| seed | 2026-09-05 | 132 | 132 | 0 | 1 | 132 | 1 | 0 | +| seed | 2026-09-22 | 17 | 17 | 0 | 0 | 17 | 0 | 0 | +| seed | 2026-09-29 | 222 | 222 | 0 | 0 | 222 | 0 | 0 | +| unified | codex | 314 | 311 | 3 | 0 | 303 | 0 | 3 | +| unified | 2026-09-05 | 183 | 183 | 0 | 1 | 132 | 1 | 0 | +| unified | 2026-09-22 | 171 | 171 | 0 | 1 | 17 | 0 | 0 | +| unified | 2026-09-29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | diff --git a/docs/judge_isolation_2026-09-30/independent/transcripts.py b/docs/judge_isolation_2026-09-30/independent/transcripts.py new file mode 100644 index 00000000..a0102a87 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/independent/transcripts.py @@ -0,0 +1,239 @@ +"""Transcript parsing helpers for the independent judge-isolation audit. + +Reads Claude Code session/subagent JSONL transcripts and Codex text logs. +Read-only: never writes outside the caller's chosen output paths. +""" +from __future__ import annotations + +import json +import re +from pathlib import Path + +TOOL_PART_TYPES = {"tool_use", "server_tool_use", "mcp_tool_use"} +CASE_RE = re.compile(r"us__scenario_\d{3}__[a-z0-9_]+") + + +def canon(obj) -> str: + """Canonical form used for content binding (parsed-JSON equality).""" + return json.dumps(obj, sort_keys=True, ensure_ascii=False, separators=(",", ":")) + + +def load_lines(path: Path): + out = [] + with open(path, "rb") as fh: + for raw in fh: + raw = raw.strip() + if not raw: + continue + try: + out.append(json.loads(raw)) + except Exception: + out.append({"_unparseable": raw[:200].decode("utf-8", "replace")}) + return out + + +def _text_of(content) -> str: + if content is None: + return "" + if isinstance(content, str): + return content + if isinstance(content, list): + parts = [] + for p in content: + if isinstance(p, dict): + if p.get("type") == "text": + parts.append(p.get("text", "")) + elif p.get("type") == "tool_reference": + parts.append(f"[tool_reference {p.get('tool_name')}]") + else: + parts.append(json.dumps(p)[:2000]) + else: + parts.append(str(p)) + return "\n".join(parts) + return json.dumps(content) + + +def parse_claude(path: Path) -> dict: + """Summarise one Claude transcript: tool calls, results, prompts, ids.""" + rows = load_lines(path) + calls, seen = [], set() + results = {} + user_strings = [] # (index, text) of plain user prompts, in order + session_ids, agent_ids, cwds = set(), set(), set() + models = set() + first_ts = last_ts = None + sidechain_rows = 0 + injected = [] + final_texts = [] + for i, d in enumerate(rows): + if "sessionId" in d: + session_ids.add(d["sessionId"]) + if d.get("agentId"): + agent_ids.add(d["agentId"]) + if d.get("cwd"): + cwds.add(d["cwd"]) + ts = d.get("timestamp") + if ts: + first_ts = first_ts or ts + last_ts = ts + if d.get("isSidechain"): + sidechain_rows += 1 + msg = d.get("message") if isinstance(d.get("message"), dict) else None + typ = d.get("type") + if typ == "attachment": + att = d.get("attachment") or {} + injected.append(att.get("type")) + if not msg: + continue + content = msg.get("content") + if typ == "assistant": + if msg.get("model"): + models.add(msg["model"]) + if isinstance(content, list): + for p in content: + if not isinstance(p, dict): + continue + if p.get("type") in TOOL_PART_TYPES: + tid = p.get("id") or f"{i}:{p.get('name')}" + if tid in seen: + continue + seen.add(tid) + calls.append({ + "id": tid, + "name": p.get("name"), + "input": p.get("input"), + "row": i, + "ts": ts, + "part_type": p.get("type"), + "sidechain": bool(d.get("isSidechain")), + }) + elif p.get("type") == "text": + final_texts.append((i, p.get("text", ""))) + elif typ == "user": + if isinstance(content, str): + user_strings.append((i, content)) + elif isinstance(content, list): + for p in content: + if not isinstance(p, dict): + continue + if p.get("type") == "tool_result": + results[p.get("tool_use_id")] = { + "is_error": bool(p.get("is_error")), + "text": _text_of(p.get("content")), + "row": i, + } + elif p.get("type") == "text": + user_strings.append((i, p.get("text", ""))) + return { + "path": str(path), + "calls": calls, + "results": results, + "user_strings": user_strings, + "session_ids": sorted(session_ids), + "agent_ids": sorted(agent_ids), + "cwds": sorted(cwds), + "models": sorted(models), + "first_ts": first_ts, + "last_ts": last_ts, + "sidechain_rows": sidechain_rows, + "n_rows": len(rows), + "injected": injected, + "final_texts": final_texts, + } + + +def structured_outputs(parsed: dict): + return [c for c in parsed["calls"] if c["name"] == "StructuredOutput"] + + +# ---------------------------------------------------------------- codex logs + +STDERR_NOISE = re.compile( + r"^(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}\.\d+Z\s+(ERROR|WARN|INFO|DEBUG|TRACE)\b.*" + r"|warning: .*" + r"|codex-shim: .*" + r"|Reading prompt from stdin\.\.\.)$" +) + + +def parse_codex(path: Path) -> dict: + txt = path.read_text(encoding="utf-8", errors="replace") + lines = txt.split("\n") + header = {} + for ln in lines[:20]: + m = re.match(r"^(workdir|model|provider|approval|sandbox|reasoning effort|reasoning summaries|session id): (.*)$", ln) + if m: + header[m.group(1)] = m.group(2) + blocks = [] # (index, header word) + block_words = {"user", "codex", "exec", "thinking", "tokens used", "mcp", "tool", "apply_patch", + "file update", "web search", "web_search", "plan update", "turn diff"} + for i, ln in enumerate(lines): + if ln in block_words: + blocks.append((i, ln)) + execs = [] + for bi, (i, w) in enumerate(blocks): + if w == "exec": + end = blocks[bi + 1][0] if bi + 1 < len(blocks) else len(lines) + execs.append("\n".join(lines[i + 1:end]).strip()) + # other tool-ish markers codex prints inline + tool_markers = [ln for ln in lines if re.match(r"^(🌐|🔎|✓|✗|mcp: |tool |web search|Searched|apply_patch)", ln)] + user_idx = [i for i, w in blocks if w == "user"] + codex_idx = [i for i, w in blocks if w == "codex"] + tok_idx = [i for i, w in blocks if w == "tokens used"] + prompt = None + noise = [] + if user_idx: + end = min([i for i, _ in blocks if i > user_idx[0]] or [len(lines)]) + raw = lines[user_idx[0] + 1:end] + kept = [] + for ln in raw: + if STDERR_NOISE.match(ln): + noise.append(ln) + else: + kept.append(ln) + prompt = "\n".join(kept) + final = None + final_obj = None + if tok_idx: + final = "\n".join(lines[tok_idx[-1] + 2:]).strip() + try: + final_obj = json.loads(final) + except Exception: + final_obj = None + last_msg_obj = None + if codex_idx: + s = codex_idx[-1] + 1 + e = tok_idx[-1] if tok_idx and tok_idx[-1] > s else len(lines) + body = "\n".join(lines[s:e]).strip() + try: + last_msg_obj = json.loads(body) + except Exception: + last_msg_obj = None + return { + "path": str(path), + "header": header, + "blocks": [w for _, w in blocks], + "execs": execs, + "tool_markers": tool_markers, + "prompt": prompt, + "stderr_noise_in_prompt": noise, + "final_obj": final_obj, + "last_msg_obj": last_msg_obj, + } + + +# ------------------------------------------------------------- read content + +_LINE_PREFIX = re.compile(r"^\s*(\d+)(?:\t|→)(.*)$") + + +def reconstruct_read(texts): + """Rebuild file lines from one or more Read tool results (cat -n format).""" + got = {} + for t in texts: + t = re.sub(r".*?", "", t, flags=re.S) + for ln in t.split("\n"): + m = _LINE_PREFIX.match(ln) + if m: + got[int(m.group(1))] = m.group(2) + return got diff --git a/docs/judge_isolation_2026-09-30/independent/verdicts_indep.csv.gz b/docs/judge_isolation_2026-09-30/independent/verdicts_indep.csv.gz new file mode 100644 index 0000000000000000000000000000000000000000..4f2da4d962b41c44dd40d95bcc593afbbdcf1e65 GIT binary patch literal 52986 zcmZ^~Q*dQryDl2rHafODb~?6g+qRu_Y}>YN+qTW_GkdN7-+SMjn^etNsmjPVUOf5; zqoAOE!*p8$fx4PF8e14SJLy^28k^YD8#%cGU3J?y;f*$vzxj~7@Gm(w5ibOhK6W#g zFN@AK)Ldh4b~yZ+&DOC3$*dL8W1HjMhF7e+$=>aus06N4r~-jutn6$^M@UBrn7DCj zn0d@fo#JWN2(dqy;>w!&8i#!3%c`PhpdYv}Ze+%^YR#ZyFO_QKsE|q&`na|L}%0H@*mnn9qr;8Dn-C*Ai z^)M@Q554d6C7D%b8KxkD%YD^#YiRBjl`o~Nc0!`2KQb-RS5hCRKbF0Lse74sY8S-} zzb)G}yK<||qF~?d!>8li= zs#0hj(Ju6nC|qKySdfv)sgfH~CXk2P%VOpU6mA(0R(=^nsZc^$cR?>eP}}l+@aU7zK`3Zc2DP?|ZuL_a2mw&VP2u>gn`a68P7L zLcHY`h&qro`Muj+4iA4FjZR-I*wF7Uf7`cf=-OtqI(fDuEMNWje%`dJyyUcnP&U<} zn}&g_`e5MybC-JYems2TryF{~W1*1#I6T}5Gld56c?ZkmYy(6H@BMuc>|fvfw;e>- 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z4|a7|JR+38&KfMcL7C%OW8ZJG&Ui#=R^g#M^WbfDC0mhycz!iSUv$nFkl%OjSDW&Z zob&Nbx4GLnSnF;45T2aq{ zgH&L}swMgR47wxEeYgx>?=U!O&1e!?G25WqI$WNu)1&u0dRrJiT}I?@{&VE01J58z zwj?x3W-#$dC?Pdrq;XSiFiO_j!6=x{C`!hf9{3tbP!2~~1e+tCXebFdiBhmBh?E3# zjSYN#?Sx4^2M!FVid73Upqh>4j?xC9EC^*y8_LR}w|jW+AZh~;eX0No0ENk+(E9Dx ze2%)=mA#$;XYFQDfA>=>ez5&1` interleaves into the log; otherwise the prompts are identical. +- **Claude prompts match:** all 724 Claude judges saw text identical to the published prompt. 237 seed Workflow judges read it from another tree (adds0928-provisional 201, v2 13, final 7, unified 16), with identical content. +- **Sidecar integrity:** all 1,342 `verdict_sha256` values match. All 221 `prompt_sha256` values and all agent and workflow ids match. +- **Weak sidecar ids:** 184 Workflow sidecars from 2026-09-22/23 carry no workflow or agent id; 12 of them instead carry a workflow id as `session_id`, and their parent session `3c78d187` isn't named anywhere. I found all 184 agents by content. +- **Context that reached judges without a tool call:** none of these mentions the case except one, and I did not mark any of them as affected. + - The workflow-authoring skill text was injected as a user turn into all 318 CLI sessions (319 rows). The one judge that delegated used the Workflow tool that text describes. + - The git status went into 32 CLI rows. For `scenario_045 snap` (live) it lists SNAP commits: "Exclude the SNAP child support defect (r33)" and "Add a note on the five SNAP BBCE households…". + - `~/PolicyEngine/CLAUDE.md` and the repo's `CLAUDE.md` were auto-loaded into 12 Workflow judges, 4 of them live. Neither file mentions the case, the audit or verdicts. + - The parent session's user request was relayed to Workflow judges. For 9 unified income-tax judges (not live), that request was about SNAP and BBCE. +- **Isolation was by instruction only for Workflow judges:** the sampled `wf_d0fdd77d` judge had Bash, Edit, Write and Skill defined and didn't use them. + +DONE \ No newline at end of file diff --git a/docs/judge_isolation_2026-09-30/review_redteam.md b/docs/judge_isolation_2026-09-30/review_redteam.md new file mode 100644 index 00000000..71371a06 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/review_redteam.md @@ -0,0 +1,81 @@ +# Red-team of the PolicyBench judge-isolation audit + +I found no second live verdict that used tools. But the audit claims more than its evidence supports in several places. Two of the six conclusions are misstated, and one hides a real provenance gap. I modified no files; everything below comes from reads and greps. + +## Confirmed problems + +**1. The counts in conclusion 1 don't match the audit's own `verdicts.csv`.** *Changes 1.* +- The file binds 611 Codex, 319 run_audit_claude.sh and 405 Workflow verdicts, not 611 / 315 / 390. +- That totals 1,335 bound rows, plus 7 unbound, for 1,342 rows (matching `summary.json`). +- The 4 extra run_audit_claude.sh verdicts are: + - `us__scenario_045__snap` (seed and unified, judged 2026-09-24; `verdicts.csv:272`, `:944`) + - `us__scenario_015__federal_income_tax_before_refundable_credits` (unified; `:770`) + - `us__scenario_082__payroll_tax` (seed, judged 2026-09-29; `:456`) + +**2. Conclusion 6 assigns waves to runners by date alone.** *Changes 6.* +- `wave_of()` (`audit_judge_isolation.py:66-71`) puts those run_audit_claude.sh judges into the "2026-09-22" and "2026-09-29" waves, which conclusion 6 calls Workflow-subagent waves. +- Two of them are in the release: 045 snap and 082 payroll. +- They had tools available. `run_audit_claude.sh:130` blocks only Bash, Edit, Write, NotebookEdit, WebFetch, WebSearch and Agent, which leaves Read, Grep, Glob, Workflow, ToolSearch and TaskOutput. +- They ran from git worktrees. 082 ran in the release worktree itself (transcript `9b0255fa…jsonl` shows `"gitBranch":"adds-0928-stage2"`). +- Both made zero tool calls, so their verdicts stay clean. + +**3. The 2026-09-05 judges were pushed toward tool use, and the classifier can't see how.** *Doesn't change 2, but the context was not benign.* +- Every judge transcript contains a hidden user message that loads the `workflow-authoring` skill. It says: "When a system-reminder confirms ultracode is on … author and run a workflow for every substantive task by default" (`-Users-maxghenis-PolicyEngine-policybench-wt-newmodels/795387a7…jsonl` lines 8-9). An `ultra_effort_enter` attachment comes with it. The same injection is in 082's transcript (`9b0255fa…jsonl` lines 7-11, 23). +- `classify_claude` (lines 225-237) records only attachment events, so this never appears in `instructions_files` or `attachment_kinds`. +- `judge_cwd_in_git_repo` is blank for all 315 of these verdicts. The events show `cwd` `policybench-wt/newmodels` and `"gitBranch":"add-new-models-2026-09"`, so they ran in a git worktree with Read, Grep, Glob and Workflow on. That is the mechanism behind 086. +- I checked independently. Among all transcripts in that project, the only judge that called anything besides StructuredOutput is `aed1cb53` (086) and its 4 workflow agents. The other four sessions with tool calls (`5df551ff`, `bd624e7b`, `4ad7006d`, `461fa671`) are orchestrator sessions using Bash, and none of them appears in `verdicts.csv`. + +**4. Conclusion 3's "earlier answer" framing is unsupported.** *Changes 3; these three live verdicts have unknown provenance.* +- The runner rewrites `codex.log` on every exec (`run_audit_codex.sh:86`). It only runs a case that has no valid verdict (line 70), and it writes the verdict and the log together (lines 87-91). So the runner cannot leave a verdict that differs from its own log. +- `codex_run.log` (636 `[ok]` lines, "636/636 verdicts present") and `codex_run2.log` never list 014 or 015 `federal_refundable_credits`. Those verdicts existed before any logged run. +- `015__head_medicaid_eligible` is `[ok]` in both runs (`codex_run.log:96`, `codex_run2.log:85`). Its `codex.log` is most likely run 2's, the latest, not an earlier answer. Something after run 2 replaced the verdict. +- The verdict writes "Indiana’s" with a curly apostrophe; the log writes "Indiana's". +- All three sidecars say `"backfilled_from": "codex.log"`. Their runner and model (`gpt-5.6-sol`) were assigned after the fact, from a log that does not hold the verdict. +- No codex session store exists for those accounts (`~/.codex-3/sessions` and `~/.codex-4/sessions` are absent; the runner used `--ephemeral`). + +**5. All 303 Codex sidecars in the release are backfilled.** *Weakens 1 and 5.* +- Each has `"backfilled_from": "codex.log"`. `judged_at_utc` is the backfill time, not the judging time: dozens share `2026-08-18T01:27:48.1…`. So the "2026-08-18 to 2026-09-02" date range is not evidence. +- None records `prompt_sha256`, so the audit never checked that Codex verdicts were judged on the published prompt. The log echoes the prompt. I compared one by eye (`us__scenario_000__payroll_tax`), and it matched. + +**6. The Codex classifier can't detect MCP or web-search calls.** *Latent bug; 5 still holds on direct evidence.* +- The catch-all regex `^([a-z_ ]+)$` (line 353) can't match lines with a colon, such as `mcp: codex_apps/github.create_pull_request started` or `web search: …`. +- Codex 0.144.0 does print those lines (`~/m6-sol-lanes/sol-ce-purpose-prod-r1-a.out.err.log:21116-21129`, version on line 2; `remarr-r2.log:39886`). +- I grepped every `codex.log` in both trees for `mcp:`, `web search:`, `apply patch`, `patch:`, `tool ` and a bare `exec`: zero hits. Every log has exactly three lone-word lines (`user`, `codex`, `tokens used`): 1,275 in 425 files (seed) and 1,464 in 488 files (unified). + +**7. 086's adjudication reads aren't counted as adjudication.** *Conclusion 2 names the files, but `summary.json` understates it.* +- `path_class` flags only paths containing "adjudicat" or "decision" (lines 171-172). So the 086 row (`:475`) has `read_adjudication_records=0`. +- Yet its delegates read `us_case_reference_explanations.csv`, and conclusion 2 says they also consumed `us_case_notes.csv`. That file holds earlier judges' case verdicts: its columns include `case_failure_sources`, `reference_suspect` and `case_annotation`. + +**8. The context claim in conclusion 6 isn't backed by captured content.** *Weakens 6.* +- Twelve Workflow verdicts had `PolicyEngine/CLAUDE.md` and `policybench/CLAUDE.md` injected as nested memory (`verdicts.csv` rows 259, 542, 827, 830, 931, 1026, 1031, 1047, 1124, 1210, 1221, 1322). +- `injected_instructions.json` holds neither file. Its 7 entries are 4 versions of the global CLAUDE.md, the global AGENTS.md and two MEMORY.md files. +- I read the current versions of both: no case facts. The versions in effect at judging time were never captured. + +**9. Workflow judges read prompts from other trees.** *Residual risk; doesn't change any conclusion.* +- Their Read targets are under `adds0928-provisional`, `adds0928-v2` or `unified_audit`, never the published `adds0928-v3`. `path_class` (line 168) counts any `/audit/cases//prompt.md` as the judge's own prompt. +- For the 2026-09-29 wave, `write_verdicts.py:68` compared the prompt bytes only at write time. For the 2026-09-22 wave, `judge_stages.py` `stage_write` does no prompt check at all. +- The audit could have compared the Read result stored in each transcript, but didn't. One spot check (`us__scenario_014__payroll_tax`) shows the same prompt hash in both trees today. + +**10. The git-repo flag is wrong for Workflow agents.** *Minor.* +- They show `judge_cwd_in_git_repo=False` while running in git worktrees: row 259 (`opus55`, False) against row 770 (same directory, True). The agent transcripts record `"gitBranch":"HEAD"`. + +## Checked and found sound +- **Sidecar hashes:** every sidecar hashes to its verdict. Where a prompt hash was recorded, none differs from the current `prompt.md` (tested on the first hex character, which catches 15 of 16 mismatches). +- **Workflow binding:** + - Every `bound_agents_same_answer` is 1. + - Every agent id matches its transcript path, and the Workflow column matches the path. + - The one re-judge (`067__dependent1_medicare_eligible#r1`) is bound to the re-judge agent. Its extra instruction only names the hedge ("rationale: 'may be'"). +- **Live copy against the Logpile copy for 086:** both hold the same 4 tool calls (Workflow, ToolSearch, TaskOutput, StructuredOutput). Its 4 workflow agents are present. +- **Tool-call shapes:** the classifier catches any content type ending in `tool_use` (server and MCP tools included). Any unknown tool name, including TaskOutput, Monitor and ToolSearch, marks the verdict affected. +- **Workflow agent task text:** in the judge workflows, none contains adjudication, decisions, `reference_suspect`, `us_case_`, `annotations/`, `policyengine-us` or `verdict.json`. Other workflows in that session do contain such terms (e.g. `wf_7fdf90ed`, `wf_0d2f95b5`), but none is bound to a published verdict. +- **Published `prompt.md` files (674):** "adjudicated" appears only in the shared boilerplate on line 3. Nothing else carries decisions, notes or rulings. The "AUTHORITATIVE ENGINE FACTS" sections are engine facts, not decisions. 182 prompts and 182 verdicts include `ox-alpha`. +- **Injected global CLAUDE.md, AGENTS.md and MEMORY.md:** none mentions PolicyBench case content. +- **Conclusion 4:** the main-session verdict is not in the release (row 784, `live_in_published_release=False`). +- **Codex context:** there is no `AGENTS.md` in the repo, in `~/PolicyEngine`, or in `~/.codex-3`. Codex did load a skills list (a "Skill descriptions were shortened" warning appears in the logs), which I did not inspect. + +## Not established +- Whether the Workflow judges saw the published prompt bytes (item 9). +- What produced the three unbound verdicts (item 4). +- The exact CLAUDE.md contents at judging time (item 8). + +I couldn't write the plan file or exit plan mode because those tools weren't available in this session. \ No newline at end of file diff --git a/docs/judge_isolation_2026-09-30/session_contexts.json b/docs/judge_isolation_2026-09-30/session_contexts.json new file mode 100644 index 00000000..f081ec41 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/session_contexts.json @@ -0,0 +1,2667 @@ +{ + "78fe4bcaf299": { + "context": {}, + "judges": [ + [ + "seed", + "us__scenario_000__federal_income_tax_before_refundable_credits", + "2026-09-29", + "True" + ], + [ + "seed", + "us__scenario_001__federal_income_tax_before_refundable_credits", + "2026-09-29", + "True" + 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"us__scenario_120__federal_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_120__payroll_tax", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_120__state_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_121__head_medicare_eligible", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_121__snap", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_122__federal_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_122__state_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_123__child1_chip_eligible", + "2026-09-22", + "True" + ], + [ + "unified", + "us__scenario_123__federal_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_123__payroll_tax", + "2026-09-22", + "False" + ], + [ + "unified", + "us__scenario_123__state_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ] + ] + }, + "34d824030817": { + "context": { + "gitStatus": "This is the git status at the start of the conversation. Note that this status is a snapshot in time, and will not update during the conversation.\n\nCurrent branch: add-new-models-2026-09\n\nMain branch (you will usually use this for PRs): main\n\nGit user: Max Ghenis\n\nStatus:\nM app/src/modelMeta.ts\n M policybench/config.py\n M policybench/eval_no_tools.py\n M policybench/model_cards.py\n?? out.md\n?? scripts/run_audit_claude.sh\n\nRecent commits:\n5dfe535 Render the model-runs caption from frozen serving evidence\nae9dbaf Remove lane progress files from the branch\n8f928a8 Address peer review round 5: fail-closed provenance, run-state serving evidence, version-scoped app copy\n8d6b318 Regenerate serving snapshot and paper artifacts\n4a3db0e Implement peer review fixes before refreeze" + }, + "judges": [ + [ + "seed", + "us__scenario_008__child3_chip_eligible", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_008__spouse_medicaid_eligible", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_008__spouse_medicare_eligible", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_029__federal_income_tax_before_refundable_credits", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_029__head_medicaid_eligible", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_029__payroll_tax", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_048__state_income_tax_before_refundable_credits", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_049__payroll_tax", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_067__payroll_tax", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_068__payroll_tax", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_079__spouse_medicaid_eligible", + "2026-09-05", + "True" + ], + [ + "seed", + "us__scenario_084__snap", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_008__child3_chip_eligible", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_008__spouse_medicaid_eligible", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_008__spouse_medicare_eligible", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_029__federal_income_tax_before_refundable_credits", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_029__head_medicaid_eligible", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_029__payroll_tax", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_045__federal_income_tax_before_refundable_credits", + "2026-09-05", + "False" + ], + [ + "unified", + "us__scenario_048__state_income_tax_before_refundable_credits", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_049__payroll_tax", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_059__federal_income_tax_before_refundable_credits", + "2026-09-05", + "False" + ], + [ + "unified", + "us__scenario_067__dependent1_medicare_eligible", + "2026-09-05", + "False" + ], + [ + "unified", + "us__scenario_067__payroll_tax", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_068__payroll_tax", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_079__spouse_medicaid_eligible", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_084__snap", + "2026-09-05", + "True" + ], + [ + "unified", + "us__scenario_085__federal_income_tax_before_refundable_credits", + "2026-09-05", + "False" + ] + ] + }, + "b828093347b0": { + "context": { + "gitStatus": "This is the git status at the start of the conversation. Note that this status is a snapshot in time, and will not update during the conversation.\n\nCurrent branch: release-20260922c-child-support-fixed\n\nMain branch (you will usually use this for PRs): main\n\nGit user: Max Ghenis\n\nStatus:\nM annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv\n M reference_audit/2026-09-22/README.md\n M reference_audit/2026-09-22/fixes/c13v3_upstream_plus_r33.py\n M reference_audit/2026-09-22/fixes/r33_snap_child_support_treatment.py\n M reference_audit/2026-09-22/root_causes.json\n M reference_audit/2026-09-22/scripts/build_records.py\n M reference_audit/2026-09-22/scripts/make_on_convention.py\n M reference_audit/2026-09-22/scripts/package_audit.py\n M reference_audit/2026-09-22/scripts/regen_references.py\n M reference_audit/2026-09-22/scripts/run_one.sh\n\nRecent commits:\nba886b4 Exclude the SNAP child support defect (r33) in release 20260922b (#177)\na637921 Add a note on the five SNAP BBCE households for the September 22 board (#175)\n56844e2 Add Claude Opus 5.5, GPT-6 Sol and GPT-6 Luna; publish the September 22 reference audit (#174)\nc7aaffe Chart every program's exact-match rate on the model page (#171)\n80113a3 Mark the serving-sensitivity re-runs on the Claude rows instead of a banner (#167)" + }, + "judges": [ + [ + "seed", + "us__scenario_045__snap", + "2026-09-22", + "True" + ], + [ + "unified", + "us__scenario_045__snap", + "2026-09-22", + "True" + ] + ] + }, + "9670b577b0f9": { + "context": { + "gitStatus": "This is the git status at the start of the conversation. Note that this status is a snapshot in time, and will not update during the conversation.\n\nCurrent branch: adds-0928-stage2\n\nMain branch (you will usually use this for PRs): main\n\nGit user: Max Ghenis\n\nStatus:\n(clean)\n\nRecent commits:\n3def82d9 Derive the stage-2 exclusion count from the reference revision\nf4859663 Rewrite the derivation narratives of the changed references\nec22fc2d Regenerate the US references on policyengine-us 2.15.17\n81e74401 Let the stage-2 driver release a reviewed reference revision\nbe90e543 Pin policyengine.py 6.1.2 and policyengine-us 2.15.17" + }, + "judges": [ + [ + "seed", + "us__scenario_082__payroll_tax", + "2026-09-29", + "True" + ] + ] + }, + "514951795453": { + "context": { + "gitStatus": "This is the git status at the start of the conversation. Note that this status is a snapshot in time, and will not update during the conversation.\n\nCurrent branch: add-opus-5.5\n\nMain branch (you will usually use this for PRs): main\n\nGit user: Max Ghenis\n\nStatus:\nM scripts/freeze_snapshot.py\n\nRecent commits:\n80a718d Prepare the 40-model board copy, note, and freezer paths for Opus 5.5\n87707b1 Add the Claude Opus 5.5 model card and metadata\nc7aaffe Chart every program's exact-match rate on the model page (#171)\n80113a3 Mark the serving-sensitivity re-runs on the Claude rows instead of a banner (#167)\n269c22f Name the row GLM-5.3-Flash (preview) and footnote what it was queried as (#170)" + }, + "judges": [ + [ + "unified", + "us__scenario_015__federal_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ] + ] + }, + "1115b76893c0": { + "context": {}, + "judges": [ + [ + "unified", + "us__scenario_091__state_income_tax_before_refundable_credits", + "2026-09-22", + "False" + ] + ] + } +} \ No newline at end of file diff --git a/docs/judge_isolation_2026-09-30/tool_calls.csv b/docs/judge_isolation_2026-09-30/tool_calls.csv new file mode 100644 index 00000000..aaf0be24 --- /dev/null +++ b/docs/judge_isolation_2026-09-30/tool_calls.csv @@ -0,0 +1,468 @@ +tree,case_id,wave,tool,category,target,path_class,result_is_error,result_preview +seed,us__scenario_000__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_000__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_001__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_001__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_003__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_003__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_004__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_004__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_005__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_005__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_005__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_005__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_005__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_005__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_007__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_007__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_007__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_007__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_007__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_007__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_007__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_007__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_008__child5_head_start_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_008__child5_head_start_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_008__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_008__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_008__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_008__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_008__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_008__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_008__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_008__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_008__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_008__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_009__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_009__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_009__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_009__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_012__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_012__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_012__free_school_meals_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_012__free_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_012__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_012__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_012__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_012__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_013__head_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_013__head_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_013__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_013__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_013__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_013__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_014__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_014__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_014__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_014__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_014__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_014__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_015__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_015__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_015__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_015__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_015__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_015__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_018__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_018__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_018__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_018__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_020__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_020__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_022__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_022__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_022__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_022__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_022__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_022__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_023__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_023__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_023__head_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_023__head_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_023__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_023__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_023__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_023__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_023__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_023__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_023__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_023__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_025__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_025__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_025__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_025__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__child1_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__child1_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__child1_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__child2_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__child2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__child2_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__child2_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__child3_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__child3_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__child3_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__child3_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_026__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_026__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_027__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_027__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_027__spouse_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_027__spouse_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_028__child1_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_028__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_028__child2_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_028__child2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_028__child3_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_028__child3_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_028__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_028__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_028__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_028__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_028__reduced_price_school_meals_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_028__reduced_price_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_028__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_028__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_029__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_029__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_030__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_030__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_031__head_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_031__head_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_032__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_032__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_032__free_school_meals_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_032__free_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_032__head_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_032__head_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_032__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_032__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_032__reduced_price_school_meals_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_032__reduced_price_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_032__spouse_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_032__spouse_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_032__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_032__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_033__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_033__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_036__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_036__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_036__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_036__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_037__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_037__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_037__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_037__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_038__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_038__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_038__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_038__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_038__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_038__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_038__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_038__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_039__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_039__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_039__head_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_039__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_039__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_039__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_042__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_042__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_042__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_042__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_043__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_043__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_043__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_043__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_043__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_043__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_044__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_044__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_044__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_044__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_045__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_045__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_045__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_045__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_045__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_045__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_046__child1_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_046__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_046__child2_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_046__child2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_046__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_046__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_046__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_046__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_046__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_046__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_049__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_049__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_049__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_049__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_051__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_051__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_051__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_051__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_052__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_052__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_053__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_053__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_053__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_053__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_053__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_053__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_054__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_054__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_054__head_wic_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_054__head_wic_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_054__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_054__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_055__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_055__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_056__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_056__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_056__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_056__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_057__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_057__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_057__head_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_057__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_057__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_057__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_057__spouse_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_057__spouse_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_057__ssi,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_057__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_057__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_057__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_059__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_059__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_062__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_062__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__child1_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_064__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__child1_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_064__child1_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__dependent1_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_064__dependent1_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__dependent2_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_064__dependent2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__dependent2_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_064__dependent2_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_064__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__ssi,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_064__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_064__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_064__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_066__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_066__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_066__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_066__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_067__dependent1_medicaid_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_067__dependent1_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_067__dependent1_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_067__dependent1_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_067__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_067__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_067__ssi,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_067__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_067__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_067__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_068__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_068__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_068__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_068__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_070__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_070__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_070__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_070__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_071__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_071__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_072__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_072__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_072__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_072__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_073__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_073__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_073__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_073__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_074__head_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_074__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_075__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_075__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_075__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_075__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_076__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_076__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_076__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_076__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_076__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_076__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_077__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_077__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_077__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_077__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_078__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_078__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_078__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_078__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_079__head_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_079__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_079__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_079__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_079__ssi,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_079__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_079__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_079__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_080__head_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_080__head_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_080__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_080__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_081__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_081__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_081__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_081__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_081__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_081__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_082__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_082__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_082__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_082__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_082__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_082__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_083__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_083__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_084__head_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_084__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_085__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_085__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_085__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_085__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_086__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_086__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Workflow,other_tool,,,False,Workflow launched in background. Task ID: wl5w5c17g Summary: Adversarially verify the 2026 CTC/ACTC and EITC claims behind a $0 refundable-credits diagnosis Transcript dir: ~/.claude/projects/-Users-maxghenis-PolicyEngine-policybench-wt-newmodels/aed1cb53-45c4-40e5-be7b-fdc78f9f0d07/s +seed,us__scenario_086__federal_refundable_credits,2026-09-05,ToolSearch,tool_search,select:TaskOutput,,False, +seed,us__scenario_086__federal_refundable_credits,2026-09-05,TaskOutput,other_tool,,,False,"success wl5w5c17g local_workflow completed { ""summary"": ""Adversarially verify the 2026 CTC/ACTC and EITC claims behind a $0 refundable-credits diagnosis"", ""agentCount"": 4, ""logs"": [], " +seed,us__scenario_086__federal_refundable_credits,2026-09-05,ToolSearch (delegate agent-a46298b8dd1e4045c),tool_search,select:Bash,,False,No matching deferred tools found +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=refundable_payroll_tax_credit|recovery_rebate|refundable_american_opportunity,False,Found 2 files paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json,policybench_repo_other | pattern=12824,False,No matches found +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations | pattern=refundable_payroll_tax_credit,False,50:[Omitted long matching line] 257:[Omitted long matching line] 478:[Omitted long matching line] [Showing results with pagination = limit: 3] +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-a46298b8dd1e4045c),read,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations,False,"50 us,scenario_005,federal_refundable_credits,0.0,6,""PolicyEngine calculated federal refundable credits of $0 for this benchmark household in 2026. The household's refundable credits total is the sum of five component credits: the Earned Income Tax Credit (eitc), the refundable American Opportunity " +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations | pattern=Georgia.*head of household|educator expense,False,679:[Omitted long matching line] 713:[Omitted long matching line] 727:[Omitted long matching line] 748:[Omitted long matching line] [Showing results with pagination = limit: 4] +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=15\.98|0\.1598|phase_out.*eitc|eitc.*phase,False,"policybench/case_annotations.py:28: (""credit"", ""eitc"", ""ctc"", ""refundable"", ""phaseout""), annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_notes.csv:62:us,scenario_008,federal_refundable_credits,32,llm_error,thresholds_rates,False,,""The required total is the sum of the 202" +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=eitc,False,Found 23 files tests/test_spec.py tests/test_scorer_vectors.py tests/test_scenarios.py tests/test_ground_truth.py tests/test_eval_no_tools.py tests/test_batch_eval.py tests/test_analysis.py tests/conftest.py policybench/scorer_vectors.py policybench/scenarios.py policybench/case_annotations.py polic +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,"policybench_repo_other | pattern=12824|12,824",False,"annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:804:[Omitted long matching line] uv.lock:130: { url = ""https://files.pythonhosted.org/packages/e9/a7/248e1aebe0c7810b0271e021a0f2a5eb6e78a051885b3c9df49f42a5802d/aiohttp-3.14.1-cp314-cp314-macosx_11_0" +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,"policybench_repo_other | pattern=4,427|4427|23,8[0-9]0|51,[0-9]{3}",False,annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:39:[Omitted long matching line] annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:351:[Omitted long matching line] annotations/us_full_run_20260612_policyen +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace,"annotations | pattern=scenario_086,federal_refundable_credits",False,"annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:683:[Omitted long matching line] annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_audit_row_annotations.csv:5581:us,scenario_086,federal_refundable_credits,claude-opus-4.7,llm_error,credi" +seed,us__scenario_086__federal_refundable_credits,2026-09-05,ToolSearch (delegate agent-ae2e309d6649e3cfa),tool_search,select:Bash,,False,No matching deferred tools found +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=standard_deduction|head_of_household|24150|23625,False,Found 8 files tests/test_spec.py tests/test_ground_truth.py tests/test_eval_no_tools.py tests/conftest.py policybench/scenarios.py paper/snapshot/20260501/us_scenarios.csv paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/scenarios.csv paper/snapshot/20260501/runs/us_ful +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=12824,False,Found 4 files uv.lock paper/snapshot/20260501/us_scenarios.csv paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/scenarios.csv paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=24150|17000|64850,False,Found 2 files uv.lock paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/us_scenarios.csv,policybench_repo_other | pattern=12824,False,72:[Omitted long matching line] +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-ae2e309d6649e3cfa),read,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/us_scenarios.csv,policybench_repo_other,False,"1 scenario_id,country,state,filing_status,num_adults,num_children,total_income,source_dataset,scenario_json" +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-ae2e309d6649e3cfa),read,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/us_scenarios.csv,policybench_repo_other,False,"72 scenario_086,us,GA,head_of_household,1,1,74824.0,enhanced_cps_2024,""{""""adults"""":[{""""age"""":44,""""employment_income"""":62000.0,""""inputs"""":{""""bank_account_assets"""":1342.6470947265625,""""educator_expense"""":337.5,""""employer_sponsored_insurance_premiums"""":19647.525390625,""""has_esi"""":true,""""health_insuranc" +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json,policybench_repo_other | pattern=scenario_086,False,No matches found +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Glob (delegate agent-ae2e309d6649e3cfa),search,paper/snapshot/20260501/runs/*/*,other_path | pattern=paper/snapshot/20260501/runs/*/*,False,paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/predictions.csv.gz paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/reference_outputs.csv paper/snapsh +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501,policybench_repo_other | pattern=adjusted_gross_income,False,Found 1 file paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/reference_outputs.csv,policybench_repo_other | pattern=^scenario_086,False,"1382:scenario_086,federal_income_tax_before_refundable_credits,3255.5478515625, 1383:scenario_086,federal_refundable_credits,0.0, 1384:scenario_086,payroll_tax,4743.0, 1385:scenario_086,self_employment_tax,0.0, 1386:scenario_086,state_income_tax_before_refundable_credits,2078.958740234375, 1387:scen" +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-ae2e309d6649e3cfa),read,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/reference_outputs.csv,policybench_repo_other,False,"1 scenario_id,variable,value,impact_weight 2 scenario_000,federal_income_tax_before_refundable_credits,2883.49365234375, 3 scenario_000,federal_refundable_credits,0.0," +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Glob (delegate agent-af6a431cfc17f2163),search,**/policyengine_us/variables/gov/irs/credits/ctc/**/*.py,other_path | pattern=**/policyengine_us/variables/gov/irs/credits/ctc/**/*.py,False,No files found +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,refundable_ctc|ctc_refundable|additional child tax,other_path | pattern=refundable_ctc|ctc_refundable|additional child tax,False,Found 4 files paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_notes.csv annotations/us_full_ +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace,"annotations | pattern=12824|12,824",False,"annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:804:[Omitted long matching line] annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_audit_row_annotations.csv:5559:us,scenario_086,federal_income_tax_before_refundable_credits,gemini-3.1-fl" +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations | pattern=scenario_086,False,679:[Omitted long matching line] 683:[Omitted long matching line] 693:[Omitted long matching line] 703:[Omitted long matching line] 709:[Omitted long matching line] [Showing results with pagination = limit: 5] +seed,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_notes.csv,prior_verdicts | pattern=scenario_086,False,"634:us,scenario_086,child1_chip_eligible,1,llm_error,categorical_eligibility,False,,""CHIP requires Medicaid ineligibility and satisfaction of CHIP’s independent age and income criteria. Child 1 is age 12, so age is not the failing criterion; the household income exceeds Georgia’s applicable CHIP inc" +seed,us__scenario_086__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_086__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_088__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_088__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_089__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_089__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_090__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_090__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_090__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_090__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_091__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_091__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_091__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_091__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_092__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_092__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_093__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_093__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_093__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_093__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_093__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_093__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_095__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_095__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_099__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_099__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_099__free_school_meals_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_099__free_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_099__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_099__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_099__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_099__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_100__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_100__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_100__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_100__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_100__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_100__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_100__tanf,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_100__tanf/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_101__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_101__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_102__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_102__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_102__head_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_102__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_102__spouse_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_102__spouse_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_104__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_104__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_107__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_107__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_108__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_108__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_108__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_108__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_108__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_108__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_108__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_108__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_109__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_109__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_109__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_109__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_110__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_110__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_110__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_110__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_111__spouse_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_111__spouse_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_112__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_112__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_114__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_114__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_114__self_employment_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_114__self_employment_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_114__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_114__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_115__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_115__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_117__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_117__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_117__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-final/audit/cases/us__scenario_117__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_117__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_117__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_118__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-final/audit/cases/us__scenario_118__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_118__ssi,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-final/audit/cases/us__scenario_118__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_118__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-final/audit/cases/us__scenario_118__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_119__child1_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_119__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_119__child2_chip_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_119__child2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_119__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_119__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_119__federal_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_119__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_119__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_119__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_119__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_119__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_120__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-final/audit/cases/us__scenario_120__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_120__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-final/audit/cases/us__scenario_120__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_120__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-final/audit/cases/us__scenario_120__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_121__head_medicare_eligible,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_121__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_121__snap,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_121__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_122__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_122__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_122__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_122__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_122__state_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_122__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_123__child1_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_123__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_123__federal_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_123__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_123__payroll_tax,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-provisional/audit/cases/us__scenario_123__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +seed,us__scenario_123__state_income_tax_before_refundable_credits,2026-09-29,Read,read,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v2/audit/cases/us__scenario_123__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_000__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_000__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_003__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_003__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_005__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_005__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_005__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_005__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_005__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_005__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_007__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_007__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_007__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_007__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_007__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_007__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_008__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_008__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_008__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_008__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_008__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_008__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_008__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_008__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_008__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_008__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_012__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_012__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_013__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_013__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_014__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_014__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_014__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_014__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_018__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_018__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_018__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_018__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_022__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_022__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_022__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_022__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_022__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_022__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_023__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_023__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_023__head_medicaid_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_023__head_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_023__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_023__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_023__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_023__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_023__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_023__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_023__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_023__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_025__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_025__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_026__child1_medicaid_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_026__child1_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_026__child2_medicaid_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_026__child2_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_026__child3_medicaid_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_026__child3_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_026__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_026__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_026__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_026__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_027__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_027__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_028__child1_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_028__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_028__child2_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_028__child2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_028__child3_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_028__child3_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_028__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_028__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_028__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_028__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_028__reduced_price_school_meals_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_028__reduced_price_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_028__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_028__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_029__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_029__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_030__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_030__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_031__head_medicaid_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_031__head_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_032__free_school_meals_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_032__free_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_032__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_032__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_032__reduced_price_school_meals_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_032__reduced_price_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_032__spouse_medicaid_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_032__spouse_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_032__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_032__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_033__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_033__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_036__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_036__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_038__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_038__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_038__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_038__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_038__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_038__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_038__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_038__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_039__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_039__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_039__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_039__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_039__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_039__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_042__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_042__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_042__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_042__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_043__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_043__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_043__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_043__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_043__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_043__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_044__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_044__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_045__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_045__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_045__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_045__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_046__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_046__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_049__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_049__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_051__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_051__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_051__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_051__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_052__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_052__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_053__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_053__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_054__head_wic_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_054__head_wic_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_054__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_054__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_055__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_055__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_056__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_056__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_056__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_056__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_057__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_057__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_057__ssi,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_057__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_062__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_062__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_064__child1_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_064__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_064__dependent2_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_064__dependent2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_064__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_064__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_064__ssi,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_064__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_064__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_064__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_066__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_066__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_066__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_066__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_067__dependent1_medicaid_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_067__dependent1_medicaid_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_067__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_067__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_067__ssi,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_067__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_067__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_067__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_068__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_068__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_070__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_070__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_073__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_073__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_073__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_073__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_075__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_075__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_076__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_076__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_076__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_076__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_077__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_077__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_078__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_078__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_078__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_078__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_079__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_079__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_079__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_079__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_079__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_079__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_080__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_080__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_081__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_081__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_081__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_081__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_081__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_081__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_082__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_082__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_082__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_082__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_082__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_082__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_082__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_082__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_083__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_083__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_084__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_084__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_085__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_085__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_086__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_086__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Workflow,other_tool,,,False,Workflow launched in background. Task ID: wl5w5c17g Summary: Adversarially verify the 2026 CTC/ACTC and EITC claims behind a $0 refundable-credits diagnosis Transcript dir: ~/.claude/projects/-Users-maxghenis-PolicyEngine-policybench-wt-newmodels/aed1cb53-45c4-40e5-be7b-fdc78f9f0d07/s +unified,us__scenario_086__federal_refundable_credits,2026-09-05,ToolSearch,tool_search,select:TaskOutput,,False, +unified,us__scenario_086__federal_refundable_credits,2026-09-05,TaskOutput,other_tool,,,False,"success wl5w5c17g local_workflow completed { ""summary"": ""Adversarially verify the 2026 CTC/ACTC and EITC claims behind a $0 refundable-credits diagnosis"", ""agentCount"": 4, ""logs"": [], " +unified,us__scenario_086__federal_refundable_credits,2026-09-05,ToolSearch (delegate agent-a46298b8dd1e4045c),tool_search,select:Bash,,False,No matching deferred tools found +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=refundable_payroll_tax_credit|recovery_rebate|refundable_american_opportunity,False,Found 2 files paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json,policybench_repo_other | pattern=12824,False,No matches found +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations | pattern=refundable_payroll_tax_credit,False,50:[Omitted long matching line] 257:[Omitted long matching line] 478:[Omitted long matching line] [Showing results with pagination = limit: 3] +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-a46298b8dd1e4045c),read,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations,False,"50 us,scenario_005,federal_refundable_credits,0.0,6,""PolicyEngine calculated federal refundable credits of $0 for this benchmark household in 2026. The household's refundable credits total is the sum of five component credits: the Earned Income Tax Credit (eitc), the refundable American Opportunity " +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-a46298b8dd1e4045c),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations | pattern=Georgia.*head of household|educator expense,False,679:[Omitted long matching line] 713:[Omitted long matching line] 727:[Omitted long matching line] 748:[Omitted long matching line] [Showing results with pagination = limit: 4] +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=15\.98|0\.1598|phase_out.*eitc|eitc.*phase,False,"policybench/case_annotations.py:28: (""credit"", ""eitc"", ""ctc"", ""refundable"", ""phaseout""), annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_notes.csv:62:us,scenario_008,federal_refundable_credits,32,llm_error,thresholds_rates,False,,""The required total is the sum of the 202" +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=eitc,False,Found 23 files tests/test_spec.py tests/test_scorer_vectors.py tests/test_scenarios.py tests/test_ground_truth.py tests/test_eval_no_tools.py tests/test_batch_eval.py tests/test_analysis.py tests/conftest.py policybench/scorer_vectors.py policybench/scenarios.py policybench/case_annotations.py polic +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,"policybench_repo_other | pattern=12824|12,824",False,"annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:804:[Omitted long matching line] uv.lock:130: { url = ""https://files.pythonhosted.org/packages/e9/a7/248e1aebe0c7810b0271e021a0f2a5eb6e78a051885b3c9df49f42a5802d/aiohttp-3.14.1-cp314-cp314-macosx_11_0" +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels,"policybench_repo_other | pattern=4,427|4427|23,8[0-9]0|51,[0-9]{3}",False,annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:39:[Omitted long matching line] annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:351:[Omitted long matching line] annotations/us_full_run_20260612_policyen +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-adc992571fd542c20),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace,"annotations | pattern=scenario_086,federal_refundable_credits",False,"annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:683:[Omitted long matching line] annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_audit_row_annotations.csv:5581:us,scenario_086,federal_refundable_credits,claude-opus-4.7,llm_error,credi" +unified,us__scenario_086__federal_refundable_credits,2026-09-05,ToolSearch (delegate agent-ae2e309d6649e3cfa),tool_search,select:Bash,,False,No matching deferred tools found +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=standard_deduction|head_of_household|24150|23625,False,Found 8 files tests/test_spec.py tests/test_ground_truth.py tests/test_eval_no_tools.py tests/conftest.py policybench/scenarios.py paper/snapshot/20260501/us_scenarios.csv paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/scenarios.csv paper/snapshot/20260501/runs/us_ful +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=12824,False,Found 4 files uv.lock paper/snapshot/20260501/us_scenarios.csv paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/scenarios.csv paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels,policybench_repo_other | pattern=24150|17000|64850,False,Found 2 files uv.lock paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/us_scenarios.csv,policybench_repo_other | pattern=12824,False,72:[Omitted long matching line] +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-ae2e309d6649e3cfa),read,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/us_scenarios.csv,policybench_repo_other,False,"1 scenario_id,country,state,filing_status,num_adults,num_children,total_income,source_dataset,scenario_json" +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-ae2e309d6649e3cfa),read,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/us_scenarios.csv,policybench_repo_other,False,"72 scenario_086,us,GA,head_of_household,1,1,74824.0,enhanced_cps_2024,""{""""adults"""":[{""""age"""":44,""""employment_income"""":62000.0,""""inputs"""":{""""bank_account_assets"""":1342.6470947265625,""""educator_expense"""":337.5,""""employer_sponsored_insurance_premiums"""":19647.525390625,""""has_esi"""":true,""""health_insuranc" +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json,policybench_repo_other | pattern=scenario_086,False,No matches found +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Glob (delegate agent-ae2e309d6649e3cfa),search,paper/snapshot/20260501/runs/*/*,other_path | pattern=paper/snapshot/20260501/runs/*/*,False,paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/predictions.csv.gz paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/reference_outputs.csv paper/snapsh +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501,policybench_repo_other | pattern=adjusted_gross_income,False,Found 1 file paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-ae2e309d6649e3cfa),search,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/reference_outputs.csv,policybench_repo_other | pattern=^scenario_086,False,"1382:scenario_086,federal_income_tax_before_refundable_credits,3255.5478515625, 1383:scenario_086,federal_refundable_credits,0.0, 1384:scenario_086,payroll_tax,4743.0, 1385:scenario_086,self_employment_tax,0.0, 1386:scenario_086,state_income_tax_before_refundable_credits,2078.958740234375, 1387:scen" +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Read (delegate agent-ae2e309d6649e3cfa),read,~/PolicyEngine/policybench-wt/newmodels/paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/reference_outputs.csv,policybench_repo_other,False,"1 scenario_id,variable,value,impact_weight 2 scenario_000,federal_income_tax_before_refundable_credits,2883.49365234375, 3 scenario_000,federal_refundable_credits,0.0," +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Glob (delegate agent-af6a431cfc17f2163),search,**/policyengine_us/variables/gov/irs/credits/ctc/**/*.py,other_path | pattern=**/policyengine_us/variables/gov/irs/credits/ctc/**/*.py,False,No files found +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,refundable_ctc|ctc_refundable|additional child tax,other_path | pattern=refundable_ctc|ctc_refundable|additional child tax,False,Found 4 files paper/snapshot/20260501/runs/us_full_run_20260612_policyengine_4_16_1_populace/data.json annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_notes.csv annotations/us_full_ +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace,"annotations | pattern=12824|12,824",False,"annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv:804:[Omitted long matching line] annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_audit_row_annotations.csv:5559:us,scenario_086,federal_income_tax_before_refundable_credits,gemini-3.1-fl" +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_reference_explanations.csv,annotations | pattern=scenario_086,False,679:[Omitted long matching line] 683:[Omitted long matching line] 693:[Omitted long matching line] 703:[Omitted long matching line] 709:[Omitted long matching line] [Showing results with pagination = limit: 5] +unified,us__scenario_086__federal_refundable_credits,2026-09-05,Grep (delegate agent-af6a431cfc17f2163),search,~/PolicyEngine/policybench-wt/newmodels/annotations/us_full_run_20260612_policyengine_4_16_1_populace/us_case_notes.csv,prior_verdicts | pattern=scenario_086,False,"634:us,scenario_086,child1_chip_eligible,1,llm_error,categorical_eligibility,False,,""CHIP requires Medicaid ineligibility and satisfaction of CHIP’s independent age and income criteria. Child 1 is age 12, so age is not the failing criterion; the household income exceeds Georgia’s applicable CHIP inc" +unified,us__scenario_086__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_086__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_088__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_088__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_091__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_091__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_091__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_091__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_092__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_092__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_093__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_093__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_093__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_093__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_095__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_095__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_099__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_099__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_099__free_school_meals_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_099__free_school_meals_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_099__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_099__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_100__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_100__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_100__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_100__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_100__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_100__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_100__tanf,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_100__tanf/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_101__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_101__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_104__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_104__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_107__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_107__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_108__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_108__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_108__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_108__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_108__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_108__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_109__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_109__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_109__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_109__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_110__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_110__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_110__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_110__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_112__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_112__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_114__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_114__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_114__self_employment_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_114__self_employment_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_114__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_114__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_115__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_115__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_117__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_117__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_117__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_117__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_118__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_118__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_118__ssi,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_118__ssi/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_118__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_118__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_119__child1_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_119__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_119__child2_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_119__child2_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_119__federal_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_119__federal_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_119__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_119__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_119__state_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_119__state_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_120__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_120__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_120__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_120__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_120__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_120__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_121__head_medicare_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_121__head_medicare_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_121__snap,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_121__snap/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_122__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_122__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_122__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_122__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_123__child1_chip_eligible,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_123__child1_chip_eligible/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_123__federal_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_123__federal_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_123__payroll_tax,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_123__payroll_tax/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f +unified,us__scenario_123__state_income_tax_before_refundable_credits,2026-09-22,Read,read,~/PolicyEngine/policybench/results/local/unified_audit/audit/cases/us__scenario_123__state_income_tax_before_refundable_credits/prompt.md,own_prompt,False,1 You are diagnosing why AI models missed a PolicyEngine reference value on a US/UK tax-and-benefit estimation benchmark. The models answered from parametric knowledge with no tools; PolicyEngine's microsimulation is the reference. 2 3 The reference value and its derivation are generated directly f diff --git a/docs/judge_isolation_2026-09-30/verdicts.csv b/docs/judge_isolation_2026-09-30/verdicts.csv new file mode 100644 index 00000000..417b224b --- /dev/null +++ b/docs/judge_isolation_2026-09-30/verdicts.csv @@ -0,0 +1,1343 @@ +tree,case_id,wave,runner,judged_at_utc,live_in_published_release,transcript_status,transcript,n_tool_calls,read_own_prompt,read_other_files,searched,shell,browsed,other_tools,affected,prompt_seen,judge_cwds,judge_cwd_is_git_checkout,skills_loaded,instructions_files,context_exposure +seed,us__scenario_000__federal_income_tax_before_refundable_credits,2026-09-29,workflow_subagent,2026-09-29T01:49:39.753805+00:00,True,bound,~/.claude/projects/-Users-maxghenis-Library-Application-Support-Claude-scratch-workspaces-ee3763f4-e7ea-4177-bcaf-1362266768c1-4457aa9d-2dc0-4bcf-a128-e50adae6be25-scratch-2026-09-22-c3da5e/3dd3d124-a1eb-44ed-ac89-ce3df9700101/subagents/workflows/wf_d0fdd77d-2d7/agent-aca2eeee82a84a59d.jsonl,1,1,0,0,0,0,0,no,identical,~/Library/Application Support/Claude/scratch-workspaces/ee3763f4-e7ea-4177-bcaf-1362266768c1/4457aa9d-2dc0-4bcf-a128-e50adae6be25/scratch-2026-09-22-c3da5e,no,,~/.claude/CLAUDE.md;~/.claude/projects/-Users-maxghenis-Library-Application-Support-Claude-scratch-workspaces-ee3763f4-e7ea-4177-bcaf-1362266768c1-4457aa9d-2dc0-4bcf-a128-e50adae6be25-scratch-2026-09-22-c3da5e/memory/MEMORY.md;~/AGENTS.md, +seed,us__scenario_000__head_medicaid_eligible,codex (sidecars 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+seed,us__scenario_001__federal_income_tax_before_refundable_credits,2026-09-29,workflow_subagent,2026-09-29T01:49:39.861271+00:00,True,bound,~/.claude/projects/-Users-maxghenis-Library-Application-Support-Claude-scratch-workspaces-ee3763f4-e7ea-4177-bcaf-1362266768c1-4457aa9d-2dc0-4bcf-a128-e50adae6be25-scratch-2026-09-22-c3da5e/3dd3d124-a1eb-44ed-ac89-ce3df9700101/subagents/workflows/wf_d0fdd77d-2d7/agent-a7ea3a09af5bcfd29.jsonl,1,1,0,0,0,0,0,no,identical,~/Library/Application Support/Claude/scratch-workspaces/ee3763f4-e7ea-4177-bcaf-1362266768c1/4457aa9d-2dc0-4bcf-a128-e50adae6be25/scratch-2026-09-22-c3da5e,no,,~/.claude/CLAUDE.md;~/.claude/projects/-Users-maxghenis-Library-Application-Support-Claude-scratch-workspaces-ee3763f4-e7ea-4177-bcaf-1362266768c1-4457aa9d-2dc0-4bcf-a128-e50adae6be25-scratch-2026-09-22-c3da5e/memory/MEMORY.md;~/AGENTS.md, +seed,us__scenario_001__head_medicaid_eligible,2026-09-05,claude_cli,2026-09-05T02:40:45.739113+00:00,True,bound,~/.claude/projects/-Users-maxghenis-PolicyEngine-policybench-wt-newmodels/795387a7-ed9a-4f2f-be53-a7992d7c5b76.jsonl,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench-wt/newmodels,gone,workflow-authoring,, +seed,us__scenario_001__payroll_tax,codex (sidecars backfilled),codex,2026-08-18T01:11:51.791741+00:00,True,bound,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v3/audit/cases/us__scenario_001__payroll_tax/codex.log,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench,yes,,, +seed,us__scenario_001__snap,codex (sidecars backfilled),codex,2026-08-18T01:11:43.335520+00:00,True,bound,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v3/audit/cases/us__scenario_001__snap/codex.log,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench,yes,,, +seed,us__scenario_001__spouse_medicaid_eligible,2026-09-05,claude_cli,2026-09-05T02:41:24.521751+00:00,True,bound,~/.claude/projects/-Users-maxghenis-PolicyEngine-policybench-wt-newmodels/17ccc6bb-ed2c-40aa-9a74-eb8c738fd2f8.jsonl,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench-wt/newmodels,gone,workflow-authoring,, +seed,us__scenario_001__spouse_medicare_eligible,codex (sidecars backfilled),codex,2026-08-18T01:12:47.308835+00:00,True,bound,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v3/audit/cases/us__scenario_001__spouse_medicare_eligible/codex.log,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench,yes,,, +seed,us__scenario_001__state_income_tax_before_refundable_credits,2026-09-05,claude_cli,2026-09-05T02:43:19.435290+00:00,True,bound,~/.claude/projects/-Users-maxghenis-PolicyEngine-policybench-wt-newmodels/42405682-99a9-4d58-99ca-3363007e0e8d.jsonl,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench-wt/newmodels,gone,workflow-authoring,, +seed,us__scenario_002__federal_income_tax_before_refundable_credits,2026-09-05,claude_cli,2026-09-05T02:43:30.671664+00:00,True,bound,~/.claude/projects/-Users-maxghenis-PolicyEngine-policybench-wt-newmodels/a274e023-2967-4069-8b02-01425704dd67.jsonl,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench-wt/newmodels,gone,workflow-authoring,, +seed,us__scenario_002__payroll_tax,codex (sidecars backfilled),codex,2026-08-18T01:12:47.532488+00:00,True,bound,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v3/audit/cases/us__scenario_002__payroll_tax/codex.log,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench,yes,,, +seed,us__scenario_002__spouse_medicaid_eligible,codex (sidecars backfilled),codex,2026-08-18T01:12:47.542212+00:00,True,bound,~/PolicyEngine/policybench-wt/adds0928-stage2/results/local/adds0928-v3/audit/cases/us__scenario_002__spouse_medicaid_eligible/codex.log,0,0,0,0,0,0,0,no,identical,~/PolicyEngine/policybench,yes,,, 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+unified,us__scenario_123__state_income_tax_before_refundable_credits,2026-09-22,workflow_subagent,2026-09-23T00:27:15.761708+00:00,False,bound,~/.claude/projects/-Users-maxghenis-Library-Application-Support-Claude-scratch-workspaces-ee3763f4-e7ea-4177-bcaf-1362266768c1-4457aa9d-2dc0-4bcf-a128-e50adae6be25-scratch-2026-09-22-c3da5e/3dd3d124-a1eb-44ed-ac89-ce3df9700101/subagents/workflows/wf_91d6cf16-dc7/agent-af467665f54637644.jsonl,1,1,0,0,0,0,0,no,identical,~/Library/Application Support/Claude/scratch-workspaces/ee3763f4-e7ea-4177-bcaf-1362266768c1/4457aa9d-2dc0-4bcf-a128-e50adae6be25/scratch-2026-09-22-c3da5e,no,,~/.claude/CLAUDE.md;~/AGENTS.md, From c96c693638328d1b288cec2df288d919eaf2e213 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 30 Sep 2026 09:20:38 -0400 Subject: [PATCH 02/15] Add the release 20260930b re-judge driver and freeze Re-judges exactly the five carried-over verdicts the judge-isolation audit found, with the isolated runner, and refuses any other change: carried-over verdicts, the adjudication record, references, predictions and serving evidence stay release 20260930's, every modelStats entry stays byte-identical, and the payload may differ only in the five cases' annotation fields. Co-Authored-By: Claude Opus 5.5 --- scripts/finish_rejudge_20260930b.py | 752 +++++++++++++++++++++++++ scripts/freeze_rejudge_20260930b.py | 216 +++++++ tests/test_finish_rejudge_20260930b.py | 393 +++++++++++++ 3 files changed, 1361 insertions(+) create mode 100644 scripts/finish_rejudge_20260930b.py create mode 100644 scripts/freeze_rejudge_20260930b.py create mode 100644 tests/test_finish_rejudge_20260930b.py diff --git a/scripts/finish_rejudge_20260930b.py b/scripts/finish_rejudge_20260930b.py new file mode 100644 index 00000000..365665fc --- /dev/null +++ b/scripts/finish_rejudge_20260930b.py @@ -0,0 +1,752 @@ +"""Re-judge the carried-over verdicts the 2026-09-30 judge-isolation audit found. + +The audit (``docs/judge_isolation_2026-09-30/REPORT.md``) bound every published +judge verdict to the transcript that produced it. Five verdicts carried over +into release ``dashboard-data-20260930`` cannot be presented as independent: + +- one judge used tools: it launched a Workflow whose agents searched the repo + and read this case's earlier published verdict; +- one judge was shown this case's exclusion history through the checkout's git + status; +- three verdicts have no transcript that holds their published text. + +This driver re-judges exactly those five with the isolated runner +(``scripts/run_audit_claude.sh``) and stages release ``dashboard-data-20260930b``. +Nothing else may change: every other verdict carries over byte for byte, the +adjudication record and the scoring exclusions are release 20260930's, every +model's ``modelStats`` entry and the heatmap are byte-identical, and the payload +may differ from the base only in the five cases' annotation fields. + + finish_rejudge_20260930b.py --stage-dir S --step prepare --base-stage B + CLAUDE_CONFIG_DIR=... CLAUDE_CODE_OAUTH_TOKEN=... AUDIT_ACCOUNT=... \\ + finish_rejudge_20260930b.py --stage-dir S --step judge + finish_rejudge_20260930b.py --stage-dir S --step triage + finish_rejudge_20260930b.py --stage-dir S --step export + freeze_rejudge_20260930b.py --stage-dir S [--dry-run] + +Each step refuses with SystemExit before it writes anything outside the stage. +A re-judge that flags the reference, or leaves ``llm_error`` for a scored row, +stops triage: that is a new reference question for Max, not something this +driver decides. +""" + +from __future__ import annotations + +import argparse +import gzip +import hashlib +import json +import os +import shutil +import subprocess +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "scripts")) + +import finish_gpt61sol as base_driver # noqa: E402 + +RUN_NAME = base_driver.RUN_NAME +SNAPSHOT = base_driver.SNAPSHOT +ANNOTATIONS = base_driver.ANNOTATIONS +ADJUDICATIONS = base_driver.ADJUDICATIONS +BOARD_MODELS = base_driver.BOARD_MODELS +CARRIED_USAGE = base_driver.CARRIED_USAGE +KEY = base_driver.KEY +JUDGE_MODEL = "claude-opus-5-5" + +BASE_TAG = base_driver.RELEASE_TAG +RELEASE_TAG = "dashboard-data-20260930b" +# Release 20260930 as merged to main, and its payload's sha256. Pinned from the +# merge; every step checks the checkout and the base stage against them. +BASE_COMMIT: str | None = None +BASE_SHA256: str | None = None + +# The five cases: the sha256 of the verdict the audit examined, of the prompt +# its judge saw, and why it is re-judged. prepare refuses a base stage whose +# verdict or prompt for any of them differs, so the re-judge replaces exactly +# what was audited. +NO_TRANSCRIPT = "No transcript holds the published verdict's text." +REJUDGE: dict[str, tuple[str, str, str]] = { + "us__scenario_086__federal_refundable_credits": ( + "101ad0a457764903753416f9aefa0295e8fb6de8a583407675a3e7e20f504de3", + "278696e8621e02462272b88b837c08e4a6dd4ec2518ad57360573d0fd9435d1e", + "Its 2026-09-05 judge launched a Workflow whose agents searched the repo " + "and read this case's earlier published case note and row annotations.", + ), + "us__scenario_045__snap": ( + "aaa14a511fa37c104fb0a09aa23d8168b5d3bb817264c468eda53fb17d6ef348", + "b2cbc9c82294274eaf513f55413f8551012d51b914779b579611bd5c8677cfcc", + "Its 2026-09-24 judge ran in a git checkout whose status, injected into " + "its context, named this case's r33 exclusion and fix.", + ), + "us__scenario_014__federal_refundable_credits": ( + "ad32821e5adc7fcf36c1d1587bf10514d6dbd6195cf48a5945605a22db528f64", + "77d73d79de6d24641da717e264acaa4bdefe11ad22f59daf6d9b1ac6015162f3", + NO_TRANSCRIPT, + ), + "us__scenario_015__federal_refundable_credits": ( + "20abcd0302b072f8fb64b41f73a46de45b9ae1c0e56f131e142f11a7a78037b2", + "9762bfd026cc4e732c792f3b5f0715c2b19cc73284c034b388d311fc3d2b24cd", + NO_TRANSCRIPT, + ), + "us__scenario_015__head_medicaid_eligible": ( + "5a389e3f4e8f93e1caabc888cded222f52155a41df43eebdcf41f7b087a04d34", + "16802b03cfffc8cd49b6414abad9bb1000f2797a76ffe6e6bb67b8c0473cd134", + NO_TRANSCRIPT, + ), +} + +# The payload fields a re-judge writes, per (scenario, variable, model) row. +ANNOTATION_FIELDS = frozenset( + { + "annotation", + "failureSource", + "failureSubtype", + "caseAnnotation", + "caseFailureSources", + "caseFailureSubtypes", + } +) +# What the judge's transcript may carry besides its prompt and its answer. +TRANSCRIPT_ATTACHMENTS = frozenset( + { + "environment", + "model", + "date", + "session_context", + "total_tokens_reminder", + "prompt_snapshot", + "structured_output", + "ultra_effort_enter", + "silent_turn_reminder", + } +) +STAGE = "stage.json" +AUDIT_FILES = ("verdict.json", "verdict.meta.json", "prompt.md") + +require = base_driver.require +digest = base_driver.digest +write_json = base_driver.write_json + + +def case_key(case_id: str) -> tuple[str, str]: + """(scenario_id, variable) of an audit case id.""" + _, scenario, variable = case_id.split("__", 2) + return scenario, variable + + +REJUDGE_KEYS = frozenset(case_key(case) for case in REJUDGE) + + +def require_pins() -> None: + require( + BASE_COMMIT is not None and BASE_SHA256 is not None, + "BASE_COMMIT and BASE_SHA256 are not pinned: pin release 20260930 as merged", + ) + + +def base_blob(path: str) -> bytes: + """A file as committed at BASE_COMMIT (release 20260930 on main).""" + require_pins() + result = subprocess.run( + ["git", "-C", str(ROOT), "show", f"{BASE_COMMIT}:{path}"], + capture_output=True, + ) + require( + result.returncode == 0, + f"cannot read {path} at {BASE_COMMIT[:12]}; fetch full history: " + f"{result.stderr.decode().strip()}", + ) + return result.stdout + + +def base_payload() -> dict: + """Release 20260930's payload, from BASE_COMMIT, checked against its pin.""" + snapshot = (SNAPSHOT / "data.json.gz").relative_to(ROOT).as_posix() + live = {"countries": {"us": json.loads(gzip.decompress(base_blob(snapshot)))}} + require( + hashlib.sha256(json.dumps(live).encode()).hexdigest() == BASE_SHA256, + "base payload sha256 mismatch", + ) + require( + len(live["countries"]["us"]["modelStats"]) == BOARD_MODELS, + f"base must have {BOARD_MODELS} models", + ) + return live + + +def bundle_dir(stage: Path) -> Path: + return stage / "publish" / RUN_NAME + + +def load_stage(stage: Path) -> dict: + path = stage / STAGE + require(path.is_file(), f"{path} missing: run prepare") + record = json.loads(path.read_text()) + require( + record.get("base_tag") == BASE_TAG + and record.get("base_sha256") == BASE_SHA256 + and record.get("base_commit") == BASE_COMMIT + and sorted(record.get("rejudge", [])) == sorted(REJUDGE), + "stage.json does not bind this driver's base and re-judge set", + ) + return record + + +# ---------------------------------------------------------------- prepare + + +def prepare(args) -> None: + """Copy release 20260930's stage, then set aside the five verdicts.""" + require_pins() + stage, source = args.stage_dir, args.base_stage + require(source is not None, "--base-stage is required for prepare") + require(not stage.exists(), f"{stage} exists; prepare writes a fresh stage") + pointer = json.loads(base_blob("app/src/data.artifact.json")) + require( + pointer.get("tag") == BASE_TAG and pointer.get("sha256") == BASE_SHA256, + "BASE_COMMIT's pointer is not release 20260930", + ) + base_payload() + + # The base stage must be exactly the one release 20260930's receipt binds. + receipt = json.loads((source / "release-ready.json").read_text()) + require( + receipt.get("release_tag") == BASE_TAG + and receipt.get("payload_sha256") == BASE_SHA256 + and receipt.get("partial") is False, + "the base stage's receipt is not release 20260930's", + ) + for rel, pin in receipt["files"].items(): + require( + digest(source / rel) == pin, f"base stage file changed since export: {rel}" + ) + # Its annotations are the committed ones. + source_bundle = bundle_dir(source) + for path in sorted((source_bundle / "annotations").iterdir()): + committed = f"annotations/{RUN_NAME}/{path.name}" + require( + path.read_bytes() == base_blob(committed), + f"base stage {path.name} differs from the committed {committed}", + ) + cases = source / "audit" / "cases" + for case, pins in REJUDGE.items(): + require( + digest(cases / case / "verdict.json") == pins[0], + f"{case}: the base verdict is not the audited one", + ) + require( + digest(cases / case / "prompt.md") == pins[1], + f"{case}: the base prompt is not the audited one", + ) + + stage.mkdir(parents=True) + shutil.copytree(source / "publish", stage / "publish") + shutil.copytree(source / "audit", stage / "audit") + audit = stage / "audit" / "cases" + carried = {} + for case_dir in sorted(audit.iterdir()): + for name in AUDIT_FILES: + path = case_dir / name + if path.is_file(): + carried[f"{case_dir.name}/{name}"] = digest(path) + for case, pins in REJUDGE.items(): + set_aside(audit / case, stage / "replaced-verdicts" / case, pins[2]) + bundle = bundle_dir(stage) + write_json( + stage / STAGE, + { + "base_tag": BASE_TAG, + "base_sha256": BASE_SHA256, + "base_commit": BASE_COMMIT, + "base_stage": str(source), + "release_tag": RELEASE_TAG, + "rejudge": sorted(REJUDGE), + "audit_files": carried, + "bundle_files": { + str(p.relative_to(stage)): digest(p) + for p in sorted(bundle.rglob("*")) + if p.is_file() + }, + }, + ) + print(f"Prepared {stage}: {len(REJUDGE)} verdicts set aside for re-judging") + + +def set_aside(case_dir: Path, destination: Path, reason: str) -> None: + """Move a case's verdict and its judge's records aside; never delete them.""" + destination.mkdir(parents=True, exist_ok=True) + moved = [] + for path in sorted(case_dir.iterdir()): + if path.name == "prompt.md": + continue + shutil.move(str(path), destination / path.name) + moved.append(path.name) + require("verdict.json" in moved, f"{case_dir.name}: no verdict to set aside") + (destination / "reason.txt").write_text(reason + "\n") + + +# ---------------------------------------------------------------- judge + + +def judge(args) -> None: + """Run the isolated runner on the five cases only, then validate.""" + record = load_stage(args.stage_dir) + env = dict(os.environ) + env["AUDIT_ONLY"] = " ".join(record["rejudge"]) + env["AUDIT_MODEL"] = JUDGE_MODEL + env.setdefault("AUDIT_PARALLEL", str(len(REJUDGE))) + env.setdefault("AUDIT_PYTHON", sys.executable) + subprocess.run( + [ + "bash", + str(ROOT / "scripts/run_audit_claude.sh"), + str(args.stage_dir / "audit"), + ], + env=env, + cwd=ROOT, + check=False, + ) + problems = validate(args.stage_dir, record) + require(not problems, "judge step left problems:\n" + "\n".join(problems)) + print(f"Judged {len(REJUDGE)} cases; every verdict validates") + + +# ---------------------------------------------------------------- validate + + +def prompt_model_ids(prompt: str) -> list[str]: + """The model ids a verdict must cover, from the prompt's final line.""" + marker = "using these exact model ids:" + last = prompt.rstrip().splitlines()[-1] + require(marker in last, "prompt's final line does not list the model ids") + return [m.strip() for m in last.split(marker, 1)[1].split(",") if m.strip()] + + +def transcript_problems(path: Path) -> list[str]: + """Anything in a judge transcript beyond its prompt and its answer. + + Checked here independently of the runner: a tool call other than the + structured answer, a delegate, an attachment of an unlisted kind, a + working directory inside a git repository, or a skill loaded into the + conversation. + """ + problems = [] + if not path.is_file(): + return [f"no transcript at {path}"] + for number, line in enumerate(path.read_text().splitlines(), 1): + try: + event = json.loads(line) + except ValueError: + problems.append(f"line {number} is not JSON") + continue + if event.get("type") == "attachment": + attachment = event.get("attachment") or {} + kind = attachment.get("type") + if kind not in TRANSCRIPT_ATTACHMENTS: + problems.append(f"attachment {kind!r} on line {number}") + if kind == "environment" and (attachment.get("snapshot") or {}).get( + "isGitRepo" + ): + problems.append("the judge ran inside a git repository") + if kind == "session_context" and (attachment.get("context") or {}).get( + "gitStatus" + ): + problems.append("the judge was shown a git status") + message = event.get("message") or {} + content = message.get("content") + if isinstance(content, str) and "Base directory for this skill:" in content: + problems.append(f"a skill was loaded on line {number}") + if not isinstance(content, list): + continue + for part in content: + if not isinstance(part, dict): + continue + kind = str(part.get("type", "")) + if kind.endswith("tool_use") and not ( + kind == "tool_use" and part.get("name") == "StructuredOutput" + ): + problems.append( + f"tool call {part.get('name') or kind} on line {number}" + ) + if kind == "text" and "Base directory for this skill:" in str( + part.get("text") + ): + problems.append(f"a skill was loaded on line {number}") + return problems + + +def validate(stage: Path, record: dict) -> list[str]: + """Every carried-over verdict unchanged; every re-judge isolated and complete.""" + from validate_verdict import verdict_errors + + from policybench.audit import is_hedged + + problems = [] + audit = stage / "audit" + schema = audit / "schema.json" + for rel, pin in record["audit_files"].items(): + case = rel.split("/", 1)[0] + path = audit / "cases" / rel + if case in REJUDGE and not rel.endswith("prompt.md"): + continue + if not path.is_file() or digest(path) != pin: + problems.append(f"carried-over {rel} changed or is missing") + present = { + f"{p.parent.name}/{p.name}" + for p in (audit / "cases").glob("*/*") + if p.name in AUDIT_FILES + } + extra = sorted( + rel + for rel in present - set(record["audit_files"]) + if rel.split("/", 1)[0] not in REJUDGE + ) + problems += [f"unexpected audit file {rel}" for rel in extra] + + for case, pins in REJUDGE.items(): + case_dir = audit / "cases" / case + verdict_path = case_dir / "verdict.json" + meta_path = case_dir / "verdict.meta.json" + if not verdict_path.is_file() or not meta_path.is_file(): + problems.append(f"{case}: no verdict yet") + continue + problems += [f"{case}: {e}" for e in verdict_errors(schema, verdict_path)] + verdict = json.loads(verdict_path.read_text()) + meta = json.loads(meta_path.read_text()) + prompt = (case_dir / "prompt.md").read_text() + checks = { + "the sidecar does not bind the verdict": meta.get("verdict_sha256") + == digest(verdict_path), + "the prompt is not the audited one": digest(case_dir / "prompt.md") + == pins[1], + "the sidecar does not bind the prompt": meta.get("prompt_sha256") + == pins[1], + "the verdict is the replaced one": digest(verdict_path) != pins[0], + f"the judge was not {JUDGE_MODEL}": meta.get("judge_model_reported") + == [JUDGE_MODEL], + "the runner did not record an isolated judge": ( + meta.get("judge_runner") == "scripts/run_audit_claude.sh" + and (meta.get("judge_isolation") or {}).get("transcript_tool_calls") + == 0 + ), + "the judge did not bill a subscription login": ( + (meta.get("judge_auth") or {}).get("method") + in ("oauth_token", "claude.ai") + ), + } + problems += [f"{case}: {message}" for message, ok in checks.items() if not ok] + problems += [ + f"{case}: {p}" + for p in transcript_problems(case_dir / "claude.transcript.jsonl") + ] + judged = [m.get("model") for m in verdict.get("models", [])] + expected = prompt_model_ids(prompt) + if sorted(judged) != sorted(expected) or len(set(judged)) != len(judged): + problems.append(f"{case}: models {judged} are not exactly {expected}") + texts = [verdict.get("rationale")] + [ + m.get("diagnosis") for m in verdict.get("models", []) + ] + if any(is_hedged(text) for text in texts): + problems.append(f"{case}: hedged text") + return problems + + +# ---------------------------------------------------------------- triage + + +def triage(args) -> None: + """Collect every verdict and write annotations that differ only for the five.""" + import pandas as pd + + from policybench.adjudications import ( + apply_adjudications, + excluded_case_keys, + load_adjudications, + verify_adjudications_applied, + ) + from policybench.audit import collect_audit + from policybench.reference_exclusions import ( + exclusion_keys, + load_reference_exclusions, + ) + + if hasattr(pd.options, "future") and hasattr(pd.options.future, "infer_string"): + pd.options.future.infer_string = False + stage = args.stage_dir + record = load_stage(stage) + problems = validate(stage, record) + require(not problems, "verdicts do not validate:\n" + "\n".join(problems)) + bundle = bundle_dir(stage) + annotations = bundle / "annotations" + for name in ("us_audit_row_annotations.csv", "us_case_notes.csv", ADJUDICATIONS): + rel = f"publish/{RUN_NAME}/annotations/{name}" + require( + digest(annotations / name) == record["bundle_files"][rel], + f"staged {name} changed before triage; re-run prepare", + ) + record_path = annotations / ADJUDICATIONS + require( + record_path.read_bytes() + == base_blob(f"annotations/{RUN_NAME}/{ADJUDICATIONS}"), + "the adjudication record is not release 20260930's", + ) + decisions = load_adjudications(record_path) + decided = {(d["scenario_id"], d["variable"]) for d in decisions} + require( + not decided & REJUDGE_KEYS, + f"a re-judged case has a recorded decision: {sorted(decided & REJUDGE_KEYS)}", + ) + + out = collect_audit(bundle / "us", stage / "audit") + require(out["missing"].empty and out["hedged"].empty, "missing or hedged verdicts") + rows = out["row"] + cases = out["case"].rename( + columns={ + "case_failure_source": "case_failure_sources", + "case_failure_subtype": "case_failure_subtypes", + } + ) + rows, cases, _ = apply_adjudications(rows, cases, decisions) + verify_adjudications_applied(rows, cases, decisions) + excluded = exclusion_keys(load_reference_exclusions(bundle / "us")) + require( + excluded == excluded_case_keys(decisions), + "adjudication exclusions differ from the frozen scoring exclusions", + ) + flags = cases[cases.reference_suspect.astype(bool)] + scored = ~rows[KEY].apply(tuple, axis=1).isin(excluded) + unresolved = rows[ + scored + & ~rows.failure_source.isin( + ["llm_error", "parse_contract_failure", "budget_exhausted_at_ceiling"] + ) + ] + flags.to_csv(stage / "reference-flags.csv", index=False) + unresolved.to_csv(stage / "unresolved-rows.csv", index=False) + require( + flags.empty and unresolved.empty, + "a re-judge raised a reference question: see reference-flags.csv and " + "unresolved-rows.csv. Queue it for Max (cos add-decision); do not " + "decide it here.", + ) + + base_rows = pd.read_csv(annotations / "us_audit_row_annotations.csv") + base_cases = pd.read_csv(annotations / "us_case_notes.csv") + changes = { + "rows": annotation_changes(base_rows, rows, KEY + ["model"]), + "cases": annotation_changes(base_cases, cases, KEY), + } + write_json(stage / "annotation-changes.json", changes) + rows.to_csv(annotations / "us_audit_row_annotations.csv", index=False) + cases.to_csv(annotations / "us_case_notes.csv", index=False) + # Written back through pandas, every row outside the five must keep its bytes. + for name in ("us_audit_row_annotations.csv", "us_case_notes.csv"): + require_lines_outside_rejudge_unchanged( + base_blob(f"annotations/{RUN_NAME}/{name}").decode(), + (annotations / name).read_text(), + name, + ) + print( + f"Triage complete: {len(rows)} rows / {len(cases)} cases; " + f"{len(changes['rows'])} row and {len(changes['cases'])} case " + "annotations changed" + ) + + +def annotation_changes(before, after, key: list[str]) -> list[dict]: + """Every row whose values differ; only the five cases may appear. + + The key sets must match exactly: a re-judge changes text and labels, never + which rows exist. + """ + import pandas as pd + + require(list(before.columns) == list(after.columns), "annotation columns changed") + left = before.set_index(key).sort_index() + right = after.set_index(key).sort_index() + require(left.index.equals(right.index), "annotation rows added or dropped") + changed = [] + for index in left.index: + a, b = left.loc[index], right.loc[index] + diffs = { + column: {"before": _plain(a[column]), "after": _plain(b[column])} + for column in left.columns + if not (pd.isna(a[column]) and pd.isna(b[column])) + and a[column] != b[column] + } + if diffs: + names = dict(zip(key, index if isinstance(index, tuple) else (index,))) + require( + (names["scenario_id"], names["variable"]) in REJUDGE_KEYS, + f"an annotation outside the re-judged cases changed: {names}", + ) + changed.append({**names, "changes": diffs}) + return changed + + +def _plain(value): + return ( + None if value != value else (value.item() if hasattr(value, "item") else value) + ) + + +def require_lines_outside_rejudge_unchanged(before: str, after: str, name: str) -> None: + """Byte-level check: only lines of the five cases may differ.""" + old, new = before.splitlines(), after.splitlines() + require(old[0] == new[0], f"{name}: header changed") + + prefixes = tuple(f"us,{s},{v}," for s, v in REJUDGE_KEYS) + + def outside(lines): + return [line for line in lines[1:] if not line.startswith(prefixes)] + + require( + outside(old) == outside(new), + f"{name}: a row outside the re-judged cases changed", + ) + + +# ---------------------------------------------------------------- export + + +def payload_diff(before, after, path=()) -> list[tuple]: + """Paths of every differing leaf between two JSON values.""" + if isinstance(before, dict) and isinstance(after, dict): + out = [] + for key in sorted(set(before) | set(after)): + if key not in before or key not in after: + out.append(path + (key,)) + else: + out += payload_diff(before[key], after[key], path + (key,)) + return out + if isinstance(before, list) and isinstance(after, list): + if len(before) != len(after): + return [path] + out = [] + for index, (a, b) in enumerate(zip(before, after)): + out += payload_diff(a, b, path + (index,)) + return out + return [] if json.dumps(before) == json.dumps(after) else [path] + + +def allowed_path(path: tuple) -> bool: + """A payload path a re-judge of the five cases may change.""" + # countries / us / scenarioPredictions / scenario / variable / model / field + return ( + len(path) == 7 + and path[:3] == ("countries", "us", "scenarioPredictions") + and (path[3], path[4]) in REJUDGE_KEYS + and path[6] in ANNOTATION_FIELDS + ) + + +def export(args) -> None: + """Export, then prove the payload moved only in the five cases' annotations.""" + from policybench.dashboard_schema import validate_dashboard_payload + from policybench.full_run_export import export_full_run + + stage = args.stage_dir + record = load_stage(stage) + problems = validate(stage, record) + require(not problems, "verdicts do not validate:\n" + "\n".join(problems)) + require((stage / "annotation-changes.json").is_file(), "run triage first") + bundle = bundle_dir(stage) + base_driver.verify_reference_pins(bundle / "us", "staged reference") + live = base_payload() + payload = export_full_run(bundle, countries=["us"], skip_app_data=True) + stats = payload["countries"]["us"]["modelStats"] + previous = {m["model"]: m for m in live["countries"]["us"]["modelStats"]} + for model, keys in CARRIED_USAGE.items(): + row = next(s for s in stats if s["model"] == model) + for key in keys: + row[key] = previous[model][key] + drift = base_driver.incumbent_drift(stats, previous) + require(len(stats) == BOARD_MODELS and not drift, f"modelStats drift: {drift}") + errors = validate_dashboard_payload(payload, require_failure_annotations=True) + require(not errors, f"payload validation failed: {errors[:8]}") + diff = payload_diff(live, payload) + outside = [p for p in diff if not allowed_path(p)] + require( + not outside, + f"payload changed outside the five cases' annotations: {outside[:8]}", + ) + touched = sorted({(p[3], p[4]) for p in diff}) + write_json( + stage / "payload-diff.json", + {"paths": [list(p) for p in diff], "cases": [list(t) for t in touched]}, + ) + path = stage / f"data-board{BOARD_MODELS}.json" + path.write_text(json.dumps(payload, allow_nan=False)) + shutil.copyfile(path, bundle / "data.json") + pinned = [p for p in (bundle / "annotations").iterdir() if p.is_file()] + pinned += [ + bundle / "us" / name + for name in (*base_driver.REFERENCE_FILES, "predictions.csv") + ] + pinned += [ + stage / STAGE, + stage / "annotation-changes.json", + stage / "payload-diff.json", + ] + pinned += [ + p + for p in (stage / "audit").rglob("*") + if p.is_file() + and p.name + in { + "verdict.json", + "verdict.meta.json", + "prompt.md", + "cases.jsonl", + "schema.json", + } + ] + pinned += [ + stage / "audit/cases" / case / "claude.transcript.jsonl" for case in REJUDGE + ] + write_json( + stage / "release-ready.json", + { + "release_tag": RELEASE_TAG, + "base_tag": BASE_TAG, + "base_sha256": BASE_SHA256, + "base_commit": BASE_COMMIT, + "payload_sha256": digest(path), + "models": BOARD_MODELS, + "partial": False, + "files": {str(p.relative_to(stage)): digest(p) for p in pinned}, + }, + ) + print( + f"STAGED {RELEASE_TAG}: payload {digest(path)[:12]}, no modelStats drift, " + f"{len(diff)} annotation fields changed in {len(touched)} cases" + ) + + +def parse_args(argv=None): + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--stage-dir", type=Path, required=True) + parser.add_argument( + "--step", choices=["prepare", "judge", "triage", "export"], required=True + ) + parser.add_argument("--base-stage", type=Path) + args = parser.parse_args(argv) + args.stage_dir = args.stage_dir.resolve() + if args.base_stage is not None: + args.base_stage = args.base_stage.resolve() + return args + + +def main(argv=None) -> None: + args = parse_args(argv) + {"prepare": prepare, "judge": judge, "triage": triage, "export": export}[args.step]( + args + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/freeze_rejudge_20260930b.py b/scripts/freeze_rejudge_20260930b.py new file mode 100644 index 00000000..ee897ba5 --- /dev/null +++ b/scripts/freeze_rejudge_20260930b.py @@ -0,0 +1,216 @@ +"""Freeze release dashboard-data-20260930b from a staged re-judge. + +The stage comes from ``scripts/finish_rejudge_20260930b.py``. This is a local +build: it writes the pointer, the snapshot, the annotations and +``/predictions.csv.gz``, and uploads nothing. + +Release 20260930b changes only the five re-judged cases' annotations, so the +freeze refuses anything else: + +- the export receipt must bind this stage's payload, the base and the tag, and + every file it pins must be unchanged; +- the references, the predictions of every model and the adjudication record + must equal release 20260930's; +- the serving configuration is release 20260930's, byte for byte; +- the board keeps release 20260930's snapshot date, model-response window, + version description and snapshot label, because no model and no answer changed. +""" + +from __future__ import annotations + +import argparse +import functools +import gzip +import json +import shutil +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "scripts")) + +import finish_rejudge_20260930b as driver # noqa: E402 + +RUN = driver.RUN_NAME +ADJUDICATIONS = driver.ADJUDICATIONS +BOARD_MODELS = driver.BOARD_MODELS +require = driver.require +digest = driver.digest + + +def read_json(path: Path) -> dict: + return json.loads(path.read_text()) + + +def write_json(path: Path, value: dict) -> None: + path.write_text(json.dumps(value, indent=2) + "\n") + + +def verify_receipt(stage: Path, payload_path: Path) -> dict: + """The export receipt must bind this payload, base and tag, unchanged since.""" + receipt = read_json(stage / "release-ready.json") + require( + receipt.get("release_tag") == driver.RELEASE_TAG + and receipt.get("base_tag") == driver.BASE_TAG + and receipt.get("base_sha256") == driver.BASE_SHA256 + and receipt.get("base_commit") == driver.BASE_COMMIT + and receipt.get("models") == BOARD_MODELS + and receipt.get("partial") is False, + "the export receipt does not bind this release and its base", + ) + require( + receipt.get("payload_sha256") == digest(payload_path), + "the payload changed after export", + ) + for rel, pin in receipt["files"].items(): + require(digest(stage / rel) == pin, f"{rel} changed after export") + return receipt + + +def main(argv: list[str] | None = None) -> None: + parser = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + parser.add_argument("--stage-dir", type=Path, required=True) + parser.add_argument("--dry-run", action="store_true") + args = parser.parse_args(argv) + stage = args.stage_dir.resolve() + require(stage.is_relative_to(ROOT), "--stage-dir must be inside this checkout") + driver.require_pins() + + import freeze_snapshot as freezer + import pandas as pd + + if hasattr(pd.options, "future") and hasattr(pd.options.future, "infer_string"): + pd.options.future.infer_string = False + + from policybench.adjudications import ( + load_adjudications, + verify_adjudications_applied, + ) + from policybench.dashboard_schema import validate_dashboard_payload + + driver.load_stage(stage) + payload_path = stage / f"data-board{BOARD_MODELS}.json" + verify_receipt(stage, payload_path) + payload_hash = digest(payload_path) + payload = read_json(payload_path) + recombined = json.dumps({"countries": {"us": payload["countries"]["us"]}}) + require( + recombined.encode() == payload_path.read_bytes(), + "payload does not recombine to the freeze format", + ) + errors = validate_dashboard_payload(payload, require_failure_annotations=True) + require(not errors, f"strict dashboard gate failed: {errors[:5]}") + + snapshot = ROOT / "paper/snapshot/20260501" + frozen_run = snapshot / "runs" / RUN + source_run = driver.bundle_dir(stage) + source_us = source_run / "us" + staged_annotations = source_run / "annotations" + base_manifest = json.loads( + driver.base_blob("paper/snapshot/20260501/manifest.json") + ) + + # References and every model's predictions are release 20260930's. + driver.base_driver.verify_reference_pins(source_us, "staged reference") + driver.base_driver.verify_reference_pins(frozen_run, "committed reference") + base_predictions = gzip.decompress( + driver.base_blob(f"paper/snapshot/20260501/runs/{RUN}/predictions.csv.gz") + ) + require( + base_predictions == (source_us / "predictions.csv").read_bytes(), + "staged predictions differ from release 20260930's", + ) + # The adjudication record is release 20260930's, byte for byte. + require( + (staged_annotations / ADJUDICATIONS).read_bytes() + == driver.base_blob(f"annotations/{RUN}/{ADJUDICATIONS}"), + "the adjudication record changed", + ) + adjudications = load_adjudications(staged_annotations / ADJUDICATIONS) + verify_adjudications_applied( + pd.read_csv(staged_annotations / "us_audit_row_annotations.csv"), + pd.read_csv(staged_annotations / "us_case_notes.csv"), + adjudications, + ) + cases_dir = stage / "audit" / "cases" + freezer.verify_adjudications_keep_judge_verdicts(adjudications, cases_dir) + + serving_path = "paper/snapshot/20260501/model_serving_config.json" + base_serving_bytes = driver.base_blob(serving_path) + require( + len(json.loads(base_serving_bytes)["models"]) == BOARD_MODELS, + f"release 20260930's serving configuration must list {BOARD_MODELS} models", + ) + pointer = { + "version": 1, + "repo": "PolicyEngine/policybench", + "tag": driver.RELEASE_TAG, + "asset": "dashboard-data.json", + "url": ( + "https://github.com/PolicyEngine/policybench/releases/download/" + f"{driver.RELEASE_TAG}/dashboard-data.json" + ), + "sha256": payload_hash, + "bytes": payload_path.stat().st_size, + } + versions_path = ROOT / "app/src/data.versions.json" + require( + versions_path.read_bytes() == driver.base_blob("app/src/data.versions.json"), + "data.versions.json differs from release 20260930's", + ) + if args.dry_run: + print( + f"Validated local release inputs: {driver.RELEASE_TAG}, {BOARD_MODELS} " + f"models, {payload_hash}; references, predictions, adjudications and " + "serving configuration unchanged" + ) + return + + freezer.SNAPSHOT_DATE = base_manifest["snapshot_date"] + freezer.MODEL_RESPONSE_DATE = base_manifest["model_response_date"] + freezer.SOURCE_RUN = source_run + freezer.SOURCE_US = source_us + freezer.SOURCE_ANNOTATIONS = staged_annotations + freezer.REFERENCE_META_SOURCE = source_us / "reference_outputs.csv.meta.json" + freezer.PUBLISHED_DASHBOARD_SOURCE = payload_path + freezer.PUBLISHED_DASHBOARD_ARTIFACT = { + key: pointer[key] for key in ("tag", "asset", "url", "sha256", "bytes") + } + freezer.RUN_STATE_EVIDENCE = {} + freezer.AUDIT_CASES_DIR = cases_dir + freezer.audit_judge_provenance = functools.partial( + freezer.audit_judge_provenance, cases_dir=cases_dir + ) + freezer.developer_adjudications_block = functools.partial( + freezer.developer_adjudications_block, cases_dir=cases_dir + ) + + def freeze_serving(destination: Path) -> None: + # No model changed, so every row keeps release 20260930's evidence. + destination.write_bytes(base_serving_bytes) + + freezer.freeze_serving_configuration = freeze_serving + write_json(ROOT / "app/src/data.artifact.json", pointer) + freezer.ANNOTATIONS_DEST.mkdir(parents=True, exist_ok=True) + shutil.copyfile( + staged_annotations / ADJUDICATIONS, freezer.ANNOTATIONS_DEST / ADJUDICATIONS + ) + freezer.main() + driver.base_driver.verify_reference_pins(frozen_run, "frozen reference") + require( + (ROOT / serving_path).read_bytes() == base_serving_bytes, + "the frozen serving configuration changed", + ) + cache = ROOT / "app/.cache" / f"dashboard-data-{payload_hash[:16]}.json" + cache.parent.mkdir(parents=True, exist_ok=True) + shutil.copyfile(payload_path, cache) + freezer.gzip_deterministic( + source_us / "predictions.csv", + stage / "predictions.csv.gz", + stored_name="predictions.csv", + ) + print(f"Built {driver.RELEASE_TAG} locally; payload and predictions are in {stage}") + + +if __name__ == "__main__": + main() diff --git a/tests/test_finish_rejudge_20260930b.py b/tests/test_finish_rejudge_20260930b.py new file mode 100644 index 00000000..8828a650 --- /dev/null +++ b/tests/test_finish_rejudge_20260930b.py @@ -0,0 +1,393 @@ +"""Gates of the release 20260930b re-judge driver and its freeze.""" + +from __future__ import annotations + +import csv +import hashlib +import json +import sys +from pathlib import Path + +import pytest +from hypothesis import given +from hypothesis import strategies as st + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "scripts")) + +import finish_rejudge_20260930b as driver # noqa: E402 + +CASE = "us__scenario_086__federal_refundable_credits" +OTHER = "us__scenario_000__payroll_tax" + + +def sha(data: bytes | str) -> str: + if isinstance(data, str): + data = data.encode() + return hashlib.sha256(data).hexdigest() + + +# ---------------------------------------------------------------- the pins + + +def test_the_rejudge_pins_are_the_audited_seed_verdicts(): + """Each pin is the seed's verdict and prompt, as the committed digest records.""" + with open(ROOT / "docs/gpt61sol/seed_digest.csv") as stream: + seed = {row["case_id"]: row for row in csv.DictReader(stream)} + assert len(driver.REJUDGE) == 5 + for case, (verdict_sha, prompt_sha, reason) in driver.REJUDGE.items(): + assert seed[case]["verdict_sha256"] == verdict_sha + assert seed[case]["prompt_sha256"] == prompt_sha + assert reason + + +def test_the_release_is_a_suffix_of_its_base(): + assert driver.RELEASE_TAG == driver.BASE_TAG + "b" + + +def test_every_step_refuses_before_the_base_is_pinned(monkeypatch, tmp_path): + monkeypatch.setattr(driver, "BASE_COMMIT", None) + monkeypatch.setattr(driver, "BASE_SHA256", None) + with pytest.raises(SystemExit, match="not pinned"): + driver.base_blob("app/src/data.artifact.json") + + +# ---------------------------------------------------------------- payload scope + +ROW = ( + "countries", + "us", + "scenarioPredictions", + "scenario_086", + "federal_refundable_credits", + "gpt-5.5", +) + + +@pytest.mark.parametrize("field", sorted(driver.ANNOTATION_FIELDS)) +def test_a_rejudged_cases_annotation_fields_may_change(field): + assert driver.allowed_path(ROW + (field,)) + + +@pytest.mark.parametrize( + "path", + [ + ROW + ("prediction",), + ROW + ("score",), + ROW + ("referenceExplanation",), + ( + "countries", + "us", + "scenarioPredictions", + "scenario_086", + "snap", + "gpt-5.5", + "annotation", + ), + ( + "countries", + "us", + "scenarioPredictions", + "scenario_000", + "payroll_tax", + "gpt-5.5", + "annotation", + ), + ("countries", "us", "modelStats", 0, "exact"), + ("countries", "us", "heatmap"), + ("countries", "us", "failureModes", "programs", 0, "overallCorrectPct"), + ], +) +def test_nothing_else_may_change(path): + assert not driver.allowed_path(path) + + +json_values = st.recursive( + st.none() | st.booleans() | st.integers() | st.text(max_size=5), + lambda children: ( + st.lists(children, max_size=4) + | st.dictionaries(st.text(min_size=1, max_size=4), children, max_size=4) + ), + max_leaves=20, +) + + +@given(json_values) +def test_a_value_does_not_differ_from_itself(value): + assert driver.payload_diff(value, json.loads(json.dumps(value))) == [] + + +@given( + st.dictionaries(st.text(min_size=1, max_size=4), json_values, min_size=1), st.data() +) +def test_a_changed_leaf_is_reported_at_its_path(value, data): + key = data.draw(st.sampled_from(sorted(value))) + changed = dict(value) + changed[key] = {"__changed__": True} + diff = driver.payload_diff(value, changed) + assert diff and all(path[0] == key for path in diff) + + +# ---------------------------------------------------------------- annotations + + +def test_only_the_rejudged_cases_lines_may_change(): + header = "country,scenario_id,variable,model,annotation" + before = "\n".join( + [ + header, + "us,scenario_086,federal_refundable_credits,m,old", + "us,scenario_000,payroll_tax,m,same", + ] + ) + rejudged = before.replace(",old", ",new") + driver.require_lines_outside_rejudge_unchanged(before, rejudged, "rows") + with pytest.raises(SystemExit, match="outside the re-judged cases"): + driver.require_lines_outside_rejudge_unchanged( + before, before.replace(",same", ",different"), "rows" + ) + + +def test_annotation_changes_refuse_a_change_outside_the_five(): + import pandas as pd + + key = ["scenario_id", "variable", "model"] + before = pd.DataFrame( + { + "scenario_id": ["scenario_086", "scenario_000"], + "variable": ["federal_refundable_credits", "payroll_tax"], + "model": ["m", "m"], + "annotation": ["old", "same"], + } + ) + inside = before.copy() + inside.loc[0, "annotation"] = "new" + changes = driver.annotation_changes(before, inside, key) + assert [c["scenario_id"] for c in changes] == ["scenario_086"] + outside = before.copy() + outside.loc[1, "annotation"] = "different" + with pytest.raises(SystemExit, match="outside the re-judged cases"): + driver.annotation_changes(before, outside, key) + with pytest.raises(SystemExit, match="added or dropped"): + driver.annotation_changes(before, before.iloc[:1], key) + + +# ---------------------------------------------------------------- the judge + + +def transcript(*events: dict) -> str: + return "\n".join(json.dumps(e) for e in events) + "\n" + + +ANSWER = { + "type": "assistant", + "message": { + "content": [{"type": "tool_use", "name": "StructuredOutput", "input": {}}] + }, +} +CLEAN_ENV = { + "type": "attachment", + "attachment": {"type": "environment", "snapshot": {"isGitRepo": False}}, +} + + +def test_an_isolated_transcript_passes(tmp_path): + path = tmp_path / "t.jsonl" + path.write_text(transcript(CLEAN_ENV, ANSWER)) + assert driver.transcript_problems(path) == [] + + +@pytest.mark.parametrize( + "event, problem", + [ + ( + { + "type": "assistant", + "message": { + "content": [{"type": "tool_use", "name": "Read", "input": {}}] + }, + }, + "tool call Read", + ), + ( + { + "type": "assistant", + "message": { + "content": [{"type": "tool_use", "name": "Workflow", "input": {}}] + }, + }, + "tool call Workflow", + ), + ( + { + "type": "assistant", + "message": { + "content": [{"type": "server_tool_use", "name": "web_search"}] + }, + }, + "tool call web_search", + ), + ( + { + "type": "attachment", + "attachment": {"type": "environment", "snapshot": {"isGitRepo": True}}, + }, + "git repository", + ), + ( + { + "type": "attachment", + "attachment": { + "type": "session_context", + "context": {"gitStatus": "Recent commits: r33"}, + }, + }, + "git status", + ), + ( + { + "type": "attachment", + "attachment": {"type": "nested_memory", "path": "CLAUDE.md"}, + }, + "nested_memory", + ), + ( + { + "type": "user", + "message": {"content": "Base directory for this skill: /x/skills/w-a"}, + }, + "skill", + ), + ], +) +def test_a_transcript_that_saw_or_did_more_is_refused(tmp_path, event, problem): + path = tmp_path / "t.jsonl" + path.write_text(transcript(CLEAN_ENV, event, ANSWER)) + assert any(problem in p for p in driver.transcript_problems(path)) + + +def test_a_missing_transcript_is_refused(tmp_path): + assert driver.transcript_problems(tmp_path / "absent.jsonl") + + +def test_prompt_model_ids_come_from_the_final_line(): + prompt = "facts\n\nReturn it using these exact model ids: a, b-1, c.2\n" + assert driver.prompt_model_ids(prompt) == ["a", "b-1", "c.2"] + + +# ---------------------------------------------------------------- validate + + +def make_stage(tmp_path: Path, monkeypatch) -> tuple[Path, dict]: + """A stage with one carried-over case and one re-judged case.""" + from policybench.audit import AUDIT_OUTPUT_SCHEMA + + monkeypatch.setattr(driver, "REJUDGE", {CASE: ("0" * 64, "", "reason")}) + stage = tmp_path / "stage" + cases = stage / "audit" / "cases" + (stage / "audit").mkdir(parents=True) + (stage / "audit" / "schema.json").write_text(json.dumps(AUDIT_OUTPUT_SCHEMA)) + prompt = "facts\nusing these exact model ids: m1, m2\n" + for case in (CASE, OTHER): + (cases / case).mkdir(parents=True) + (cases / case / "prompt.md").write_text(prompt) + verdict = { + "reference_suspect": False, + "reference_bug_hypothesis": "", + "case_failure_source": "llm_error", + "case_failure_subtype": "credit_phaseout", + "rationale": "The credit is absorbed by liability.", + "models": [ + { + "model": m, + "failure_source": "llm_error", + "failure_subtype": "credit_phaseout", + "diagnosis": "It paid the cap.", + } + for m in ("m1", "m2") + ], + } + for case in (CASE, OTHER): + text = json.dumps(verdict, indent=2, sort_keys=True) + (cases / case / "verdict.json").write_text(text) + meta = { + "verdict_sha256": sha(text), + "prompt_sha256": sha(prompt), + "judge_model_reported": [driver.JUDGE_MODEL], + "judge_runner": "scripts/run_audit_claude.sh", + "judge_isolation": {"transcript_tool_calls": 0}, + "judge_auth": {"method": "oauth_token"}, + } + (cases / case / "verdict.meta.json").write_text(json.dumps(meta)) + (cases / CASE / "claude.transcript.jsonl").write_text(transcript(CLEAN_ENV, ANSWER)) + monkeypatch.setattr(driver, "REJUDGE", {CASE: ("0" * 64, sha(prompt), "reason")}) + audit_files = { + f"{case}/{name}": sha((cases / case / name).read_bytes()) + for case in (CASE, OTHER) + for name in driver.AUDIT_FILES + } + return stage, {"audit_files": audit_files} + + +def test_a_clean_rejudge_validates(tmp_path, monkeypatch): + stage, record = make_stage(tmp_path, monkeypatch) + assert driver.validate(stage, record) == [] + + +def test_a_changed_carried_over_verdict_is_refused(tmp_path, monkeypatch): + stage, record = make_stage(tmp_path, monkeypatch) + path = stage / "audit/cases" / OTHER / "verdict.json" + path.write_text(path.read_text().replace("liability", "tax")) + assert any("carried-over" in p for p in driver.validate(stage, record)) + + +def test_a_rejudge_whose_judge_called_a_tool_is_refused(tmp_path, monkeypatch): + stage, record = make_stage(tmp_path, monkeypatch) + read = { + "type": "assistant", + "message": {"content": [{"type": "tool_use", "name": "Read", "input": {}}]}, + } + (stage / "audit/cases" / CASE / "claude.transcript.jsonl").write_text( + transcript(read, ANSWER) + ) + assert any("tool call Read" in p for p in driver.validate(stage, record)) + + +def test_a_rejudge_that_drops_a_model_is_refused(tmp_path, monkeypatch): + stage, record = make_stage(tmp_path, monkeypatch) + path = stage / "audit/cases" / CASE / "verdict.json" + verdict = json.loads(path.read_text()) + verdict["models"] = verdict["models"][:1] + text = json.dumps(verdict, indent=2, sort_keys=True) + path.write_text(text) + meta_path = stage / "audit/cases" / CASE / "verdict.meta.json" + meta = json.loads(meta_path.read_text()) + meta["verdict_sha256"] = sha(text) + meta_path.write_text(json.dumps(meta)) + assert any("are not exactly" in p for p in driver.validate(stage, record)) + + +def test_a_rejudge_the_runner_did_not_isolate_is_refused(tmp_path, monkeypatch): + stage, record = make_stage(tmp_path, monkeypatch) + meta_path = stage / "audit/cases" / CASE / "verdict.meta.json" + meta = json.loads(meta_path.read_text()) + del meta["judge_isolation"] + meta_path.write_text(json.dumps(meta)) + assert any("isolated judge" in p for p in driver.validate(stage, record)) + + +def test_set_aside_keeps_the_prompt_and_records_why(tmp_path): + case_dir = tmp_path / "case" + case_dir.mkdir() + for name in ("prompt.md", "verdict.json", "verdict.meta.json", "codex.log"): + (case_dir / name).write_text(name) + destination = tmp_path / "aside" + driver.set_aside(case_dir, destination, "why") + assert sorted(p.name for p in case_dir.iterdir()) == ["prompt.md"] + assert sorted(p.name for p in destination.iterdir()) == [ + "codex.log", + "reason.txt", + "verdict.json", + "verdict.meta.json", + ] + assert (destination / "reason.txt").read_text() == "why\n" From 70d5187644dd067b337e1924483515a571d5e3f8 Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 30 Sep 2026 09:21:30 -0400 Subject: [PATCH 03/15] Document release 20260930b's gates and commands Co-Authored-By: Claude Opus 5.5 --- .../release_20260930b.md | 59 +++++++++++++++++++ 1 file changed, 59 insertions(+) create mode 100644 docs/judge_isolation_2026-09-30/release_20260930b.md diff --git a/docs/judge_isolation_2026-09-30/release_20260930b.md b/docs/judge_isolation_2026-09-30/release_20260930b.md new file mode 100644 index 00000000..57e4f87b --- /dev/null +++ b/docs/judge_isolation_2026-09-30/release_20260930b.md @@ -0,0 +1,59 @@ +# Release dashboard-data-20260930b: re-judge five carried-over verdicts + +## What the release is + +Release `dashboard-data-20260930` (GPT-6.1 Sol) re-judged every case GPT-6.1 Sol re-opened with the isolated runner. It carried the other verdicts over from release 20260929 byte for byte. The [judge-isolation audit](REPORT.md) found five carried-over verdicts that cannot be presented as independent. This release re-judges those five with the same isolated runner and changes nothing else. + +| Case | Why it is re-judged | +|---|---| +| `086 federal_refundable_credits` | Its judge launched a Workflow whose agents searched the repo and read this case's earlier published case note and row annotations. | +| `045 snap` | Its judge ran in a git checkout. The checkout's git status, injected into the judge's context, named this case's r33 exclusion and fix. | +| `014 federal_refundable_credits`, `015 federal_refundable_credits`, `015 head_medicaid_eligible` | No transcript holds the published verdict's text. | + +No adjudication record covers any of the five, so the release cannot change a decision. The models, answers, references, exclusions and scores are release 20260930's. + +## What the gates enforce + +`scripts/finish_rejudge_20260930b.py` (the driver) and `scripts/freeze_rejudge_20260930b.py` (the freeze) refuse with `SystemExit` before writing outside the stage. Tests are in `tests/test_finish_rejudge_20260930b.py`. + +| Gate | Where | +|---|---| +| The base is release 20260930 as merged. `BASE_COMMIT`'s pointer names it, and its payload hashes to `BASE_SHA256`. | `prepare`, `base_payload` | +| The base stage is the one release 20260930's receipt binds: every pinned file is unchanged, and its annotations equal the committed ones. | `prepare` | +| Each of the five base verdicts and prompts is the one the audit examined. The pins equal `docs/gpt61sol/seed_digest.csv`. | `prepare`; `test_the_rejudge_pins_are_the_audited_seed_verdicts` | +| The five verdicts are set aside in `replaced-verdicts/` with the reason, never deleted. | `set_aside` | +| Every other verdict, sidecar and prompt carries over byte for byte. | `validate` | +| Each re-judge is by `claude-opus-5-5` through the isolated runner, on a subscription login, bound to its verdict and to the audited prompt. Its models are exactly the prompt's list, and no text is hedged. | `validate` | +| Each re-judge's transcript is re-checked here, independently of the runner. It fails on a tool call other than the structured answer, a git working directory, a git status, a loaded skill, or an unlisted attachment. | `transcript_problems` | +| The adjudication record is release 20260930's, byte for byte, and records no decision on the five. The scoring exclusions equal the record's. | `triage`, freeze | +| A re-judge that flags the reference, or leaves a scored row outside `llm_error`, `parse_contract_failure` and `budget_exhausted_at_ceiling`, stops triage. That is a new reference question for Max. | `triage` | +| Annotation rows and case notes outside the five are byte-identical, and no row is added or dropped. | `triage`, `annotation_changes`, `require_lines_outside_rejudge_unchanged` | +| Every model's `modelStats` entry is byte-identical to release 20260930's. | `export` | +| The payload differs from release 20260930's only in the five cases' `annotation`, `failureSource`, `failureSubtype`, `caseAnnotation`, `caseFailureSources` and `caseFailureSubtypes`. | `export`, `allowed_path` | +| The freeze accepts only a receipt that binds this release, its base and every staged file unchanged since export. References, every model's predictions, the adjudication record, the serving configuration and the version registry must equal release 20260930's. The board keeps its snapshot date and label. | freeze | + +## Commands + +The judges must bill a subfleet Claude lane's own login, never the desktop login. Run the judge step inside a lane job: +- For a keychain-token lane, point `CLAUDE_CONFIG_DIR` at an empty directory kept for the lane, and pass the lane's token without printing it: `CLAUDE_CODE_OAUTH_TOKEN="$(~/bin/agent-secret get claude-quota-)"`. +- Set `AUDIT_ACCOUNT` to the lane's account. + +`scripts/run_audit_claude.sh` refuses to start on any other login. + +```bash +export OPENBLAS_NUM_THREADS=1 PYTHONPATH="$PWD" PYTHONDONTWRITEBYTECODE=1 +PB_BASE_STAGE= +PB_STAGE="$PWD/results/local/rejudge-20260930b" + +"$PB_PY" scripts/finish_rejudge_20260930b.py --stage-dir "$PB_STAGE" --step prepare \ + --base-stage "$PB_BASE_STAGE" +CLAUDE_CONFIG_DIR= CLAUDE_CODE_OAUTH_TOKEN= \ + AUDIT_ACCOUNT= \ + "$PB_PY" scripts/finish_rejudge_20260930b.py --stage-dir "$PB_STAGE" --step judge +"$PB_PY" scripts/finish_rejudge_20260930b.py --stage-dir "$PB_STAGE" --step triage +"$PB_PY" scripts/finish_rejudge_20260930b.py --stage-dir "$PB_STAGE" --step export +"$PB_PY" scripts/freeze_rejudge_20260930b.py --stage-dir "$PB_STAGE" --dry-run +"$PB_PY" scripts/freeze_rejudge_20260930b.py --stage-dir "$PB_STAGE" +``` + +The freeze is a local build and uploads nothing. The release assets are `/predictions.csv.gz` and the staged payload renamed to `dashboard-data.json`, under tag `dashboard-data-20260930b`. After review and green CI, create the release, verify the downloaded sha256 against the pointer, and then merge. From 8168153c61d1b5ff7622cae8e61ec612f85ce71d Mon Sep 17 00:00:00 2001 From: Max Ghenis Date: Wed, 30 Sep 2026 11:18:55 -0400 Subject: [PATCH 04/15] Apply the driver review: splice annotations, bind transcripts, retry rejected re-judges - triage splices the five cases' records into release 20260930's committed CSV bytes instead of regenerating the files, so wording amendments and blank fields keep their bytes; records compare as text, field by field, and only the columns a verdict decides may change - export and the freeze re-check every annotation file against the base and against annotation-changes.json - the freeze checks the working tree is release 20260930's before writing, restores the wording-amendment record the snapshot freezer drops, and allows manifest changes only in this release's scope - each re-judge's transcript must belong to its session, open with its prompt, end with its verdict and come from Opus 5.5; the sidecar must name Opus 5.5 as requested and record a judging time - a re-judge that fails is set aside and judged again, up to three rounds; everything set aside is pinned - prepare validates the stage path and refuses base-stage audit files the receipt does not bind Co-Authored-By: Claude Opus 5.5 --- .../release_20260930b.md | 21 +- pyproject.toml | 4 +- scripts/finish_rejudge_20260930b.py | 626 +++++++++++------ scripts/freeze_rejudge_20260930b.py | 138 +++- tests/test_finish_rejudge_20260930b.py | 663 +++++++++++++----- 5 files changed, 1047 insertions(+), 405 deletions(-) diff --git a/docs/judge_isolation_2026-09-30/release_20260930b.md b/docs/judge_isolation_2026-09-30/release_20260930b.md index 57e4f87b..188e99e3 100644 --- a/docs/judge_isolation_2026-09-30/release_20260930b.md +++ b/docs/judge_isolation_2026-09-30/release_20260930b.md @@ -19,18 +19,22 @@ No adjudication record covers any of the five, so the release cannot change a de | Gate | Where | |---|---| | The base is release 20260930 as merged. `BASE_COMMIT`'s pointer names it, and its payload hashes to `BASE_SHA256`. | `prepare`, `base_payload` | -| The base stage is the one release 20260930's receipt binds: every pinned file is unchanged, and its annotations equal the committed ones. | `prepare` | +| The stage is a fresh directory under `results/local`, disjoint from the base stage. | `prepare` (`validate_stage_path`) | +| The base stage is the one release 20260930's receipt binds: every pinned file is unchanged, every verdict, sidecar and prompt it carries is one the receipt binds, and its annotation files are committed ones, byte for byte. | `prepare` | | Each of the five base verdicts and prompts is the one the audit examined. The pins equal `docs/gpt61sol/seed_digest.csv`. | `prepare`; `test_the_rejudge_pins_are_the_audited_seed_verdicts` | -| The five verdicts are set aside in `replaced-verdicts/` with the reason, never deleted. | `set_aside` | +| The five verdicts are set aside in `replaced-verdicts//published/` with the reason, never deleted. `stage.json` pins everything set aside, and every later step re-checks it. | `set_aside`, `load_stage` | +| A re-judge that fails any check below is set aside in `replaced-verdicts//round-