BE-500, BE-501: HashQL: Unify mixed parameter resolution in data dependency analysis

[indietyp]

🌟 What is the purpose of this PR?

This PR consolidates the parameter resolution logic in the data dependency analysis by unifying the handling of graph edges and constant bindings. Previously, these two sources of parameter values were handled by separate functions, but now they are processed together in a single unified approach that can handle mixed scenarios where parameters come from both sources.

🔍 What does this change?

• Removes the resolve_params_const function and merges its functionality into resolve_params
• Updates resolve_params to handle both graph edges and constant bindings simultaneously using try_reduce for consensus checking
• Adds support for the iterator_try_reduce and maybe_uninit_uninit_array_transpose library features
• Removes the unused assert_matches feature from multiple library modules
• Adds a new test case load_param_mixed that validates parameter resolution when predecessors provide a mix of constants and projections
• Improves documentation to clarify that parameter resolution now handles both graph edges and constant bindings

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

• does not modify any publishable blocks or libraries, or modifications do not need publishing

📜 Does this require a change to the docs?

The changes in this PR:

• are internal and do not require a docs change

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

• do not affect the execution graph

🛡 What tests cover this?

• New test case load_param_mixed validates the unified parameter resolution logic
• Existing data dependency analysis tests continue to cover the refactored functionality

❓ How to test this?

1. Checkout the branch
2. Run the data dependency analysis tests
3. Confirm that the new load_param_mixed test passes and validates mixed parameter resolution

[vercel]

The latest updates on your projects. Learn more about Vercel for GitHub.

Project	Deployment	Actions	Updated (UTC)
hash	Ready	Preview, Comment	May 22, 2026 11:35am
petrinaut	Ready	Preview	May 22, 2026 11:35am

2 Skipped Deployments

Project	Deployment	Actions	Updated (UTC)
hashdotdesign	Ignored	Preview	May 22, 2026 11:35am
hashdotdesign-tokens	Ignored	Preview	May 22, 2026 11:35am

[indietyp]

Warning

This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

• BE-513: HashQL: Rework dynamic aggregate size estimation #8697
• BE-512: HashQL: Allow cross-backend transitions for SwitchInt terminators #8696
• BE-524: HashQL: Remove island schedule module #8694
• BE-525: HashQL: Remove implicit type-widening from Integer operations, and make comparison size aware #8693
• BE-515: HashQL: Remove OFFSET 0 materialization fence from generated query #8650
• BE-514: HashQL: implement iterative adjustment passes in placement solver #8635
• BE-500, BE-501: HashQL: Unify mixed parameter resolution in data dependency analysis #8607 👈 (View in Graphite)
• BE-494: HashQL: Loop-breaker selection for recursive inlining #8600
• BE-482: HashQL: Remove logical not from MIR and fix postgres boolean lowering #8595
• BE-474: HashQL: Take into account terminator target eligibility in execution backend placement #8587
• BE-455: HashQL: Introduce evaluation orchestrator #8586
• BE-523: HashQL: Suspendable interpreter with structured values and input system #8675
• BE-522: HashQL: Expand stdlib type system with temporal types #8674
• BE-306: HashQL: PostgreSQL translation #8526
• BE-457: HashQL: MIR execution pipeline extensions for postgres compilation #8525
• BE-456: HashQL: Entity type model enrichment and graph store query extensions #8524
• BE-429: HashQL: Add island dependency graph with data requirement resolution #8501
• BE-428: HashQL: Simplify traversal tracking to path recording #8498
• BE-434: HashQL: Refactor entity storage path resolution with centralized schema mapping #8496
• BE-427: Add hashql-macros crate with procedural macros for ID types and symbols #8495
• main

This stack of pull requests is managed by Graphite. Learn more about stacking.

[codspeed-hq]

Merging this PR will degrade performance by 11.47%

❌ 1 regressed benchmark
✅ 23 untouched benchmarks
⏩ 56 skipped benchmarks¹

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Benchmark	BASE	HEAD	Efficiency
❌	diamond	15.8 µs	17.8 µs	-11.47%

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.

---

_{Comparing bm/be-500-hashql-forward-substitution-unified-param-resolution (4f7a295) with bm/be-494-hashql-scc-loop-breaker-inlining (8a0fbe0)²}

Footnotes

1. 56 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

2. No successful run was found on bm/be-494-hashql-scc-loop-breaker-inlining (f4b474a) during the generation of this report, so 59731d2 was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

[codecov]

Codecov Report

❌ Patch coverage is 96.25000% with 6 lines in your changes missing coverage. Please review.
✅ Project coverage is 63.31%. Comparing base (f4b474a) to head (4f7a295).

Files with missing lines	Patch %	Lines
...l/mir/src/pass/analysis/data_dependency/resolve.rs	93.33%	1 Missing and 3 partials ⚠️
...hashql/mir/src/pass/transform/inst_simplify/mod.rs	0.00%	2 Missing ⚠️

Additional details and impacted files

@@                              Coverage Diff                               @@
##           bm/be-494-hashql-scc-loop-breaker-inlining    #8607      +/-   ##
==============================================================================
- Coverage                                       63.34%   63.31%   -0.03%     
==============================================================================
  Files                                            1386     1384       -2     
  Lines                                          144941   144757     -184     
  Branches                                         5945     5935      -10     
==============================================================================
- Hits                                            91808    91660     -148     
+ Misses                                          52162    52130      -32     
+ Partials                                          971      967       -4     

Flag	Coverage Δ
apps.hash-ai-worker-ts	1.41% <ø> (ø)
apps.hash-api	0.00% <ø> (ø)
local.hash-backend-utils	2.81% <ø> (ø)
local.hash-graph-sdk	9.63% <ø> (ø)
local.hash-isomorphic-utils	0.00% <ø> (ø)
rust.hash-graph-api	2.52% <ø> (ø)
rust.hashql-compiletest	28.26% <ø> (ø)
rust.hashql-eval	79.71% <ø> (ø)
rust.hashql-mir	91.73% <96.25%> (+0.04%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
• 📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

[github-actions]

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$28.5 \mathrm{ms} \pm 196 \mathrm{μs}\left({\color{gray}-4.192 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$4.31 \mathrm{ms} \pm 45.4 \mathrm{μs}\left({\color{red}12.2 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$15.5 \mathrm{ms} \pm 154 \mathrm{μs}\left({\color{red}15.7 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$46.3 \mathrm{ms} \pm 372 \mathrm{μs}\left({\color{gray}1.79 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$15.9 \mathrm{ms} \pm 141 \mathrm{μs}\left({\color{red}7.49 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$25.5 \mathrm{ms} \pm 177 \mathrm{μs}\left({\color{gray}-4.810 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$29.3 \mathrm{ms} \pm 207 \mathrm{μs}\left({\color{gray}-2.220 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.81 \mathrm{ms} \pm 22.5 \mathrm{μs}\left({\color{gray}-3.105 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$14.2 \mathrm{ms} \pm 116 \mathrm{μs}\left({\color{gray}-0.823 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$4.13 \mathrm{ms} \pm 31.7 \mathrm{μs}\left({\color{red}9.79 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.33 \mathrm{ms} \pm 27.1 \mathrm{μs}\left({\color{red}13.6 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.85 \mathrm{ms} \pm 37.3 \mathrm{μs}\left({\color{red}16.5 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.95 \mathrm{ms} \pm 63.4 \mathrm{μs}\left({\color{red}15.5 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$4.04 \mathrm{ms} \pm 42.1 \mathrm{μs}\left({\color{gray}1.27 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.80 \mathrm{ms} \pm 53.6 \mathrm{μs}\left({\color{red}11.7 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$5.35 \mathrm{ms} \pm 69.3 \mathrm{μs}\left({\color{red}19.6 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.82 \mathrm{ms} \pm 36.9 \mathrm{μs}\left({\color{red}11.7 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$5.03 \mathrm{ms} \pm 61.1 \mathrm{μs}\left({\color{red}21.8 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.95 \mathrm{ms} \pm 23.1 \mathrm{μs}\left({\color{gray}1.88 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 30.1 \mathrm{μs}\left({\color{red}5.40 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$3.16 \mathrm{ms} \pm 60.3 \mathrm{μs}\left({\color{red}5.85 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$3.36 \mathrm{ms} \pm 37.2 \mathrm{μs}\left({\color{red}6.92 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.30 \mathrm{ms} \pm 41.5 \mathrm{μs}\left({\color{red}9.09 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$3.46 \mathrm{ms} \pm 32.9 \mathrm{μs}\left({\color{red}6.83 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.49 \mathrm{ms} \pm 40.0 \mathrm{μs}\left({\color{red}9.62 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.29 \mathrm{ms} \pm 32.2 \mathrm{μs}\left({\color{red}8.97 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$3.26 \mathrm{ms} \pm 28.5 \mathrm{μs}\left({\color{red}5.65 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$4.31 \mathrm{ms} \pm 53.4 \mathrm{μs}\left({\color{red}21.0 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.71 \mathrm{ms} \pm 43.9 \mathrm{μs}\left({\color{red}13.3 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.93 \mathrm{ms} \pm 45.9 \mathrm{μs}\left({\color{red}16.1 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.86 \mathrm{ms} \pm 40.2 \mathrm{μs}\left({\color{gray}2.85 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.21 \mathrm{ms} \pm 21.5 \mathrm{μs}\left({\color{gray}0.724 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.59 \mathrm{ms} \pm 25.9 \mathrm{μs}\left({\color{gray}2.10 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$50.0 \mathrm{ms} \pm 458 \mathrm{μs}\left({\color{red}7.98 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$89.6 \mathrm{ms} \pm 609 \mathrm{μs}\left({\color{red}6.31 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$56.7 \mathrm{ms} \pm 482 \mathrm{μs}\left({\color{red}6.98 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$55.0 \mathrm{ms} \pm 369 \mathrm{μs}\left({\color{gray}3.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$65.0 \mathrm{ms} \pm 459 \mathrm{μs}\left({\color{lightgreen}-12.502 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$47.8 \mathrm{ms} \pm 287 \mathrm{μs}\left({\color{lightgreen}-20.685 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$446 \mathrm{ms} \pm 1.05 \mathrm{ms}\left({\color{gray}1.69 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$107 \mathrm{ms} \pm 579 \mathrm{μs}\left({\color{red}10.7 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$93.4 \mathrm{ms} \pm 540 \mathrm{μs}\left({\color{gray}-2.372 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$301 \mathrm{ms} \pm 913 \mathrm{μs}\left({\color{gray}1.74 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$20.2 \mathrm{ms} \pm 145 \mathrm{μs}\left({\color{gray}-3.775 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$23.0 \mathrm{ms} \pm 256 \mathrm{μs}\left({\color{gray}3.04 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$20.0 \mathrm{ms} \pm 117 \mathrm{μs}\left({\color{lightgreen}-11.598 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$23.8 \mathrm{ms} \pm 235 \mathrm{μs}\left({\color{red}11.7 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$25.2 \mathrm{ms} \pm 165 \mathrm{μs}\left({\color{lightgreen}-5.690 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$20.5 \mathrm{ms} \pm 143 \mathrm{μs}\left({\color{gray}1.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$19.9 \mathrm{ms} \pm 130 \mathrm{μs}\left({\color{gray}1.27 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$20.2 \mathrm{ms} \pm 182 \mathrm{μs}\left({\color{gray}1.62 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$19.9 \mathrm{ms} \pm 141 \mathrm{μs}\left({\color{gray}0.088 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$26.6 \mathrm{ms} \pm 287 \mathrm{μs}\left({\color{gray}2.66 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/block/v/1	$$35.5 \mathrm{ms} \pm 327 \mathrm{μs}\left({\color{gray}3.08 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$34.9 \mathrm{ms} \pm 277 \mathrm{μs}\left({\color{gray}-1.470 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$35.6 \mathrm{ms} \pm 374 \mathrm{μs}\left({\color{lightgreen}-6.543 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$35.1 \mathrm{ms} \pm 305 \mathrm{μs}\left({\color{gray}-4.797 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$35.5 \mathrm{ms} \pm 321 \mathrm{μs}\left({\color{gray}1.09 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$36.0 \mathrm{ms} \pm 268 \mathrm{μs}\left({\color{gray}1.14 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$34.5 \mathrm{ms} \pm 316 \mathrm{μs}\left({\color{lightgreen}-7.698 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$34.5 \mathrm{ms} \pm 295 \mathrm{μs}\left({\color{gray}0.348 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$35.6 \mathrm{ms} \pm 297 \mathrm{μs}\left({\color{gray}-2.051 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.57 \mathrm{ms} \pm 50.2 \mathrm{μs}\left({\color{lightgreen}-15.874 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$100 \mathrm{ms} \pm 566 \mathrm{μs}\left({\color{lightgreen}-5.540 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$151 \mathrm{ms} \pm 563 \mathrm{μs}\left({\color{lightgreen}-12.281 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$110 \mathrm{ms} \pm 549 \mathrm{μs}\left({\color{gray}-1.060 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$122 \mathrm{ms} \pm 564 \mathrm{μs}\left({\color{gray}1.62 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$128 \mathrm{ms} \pm 474 \mathrm{μs}\left({\color{gray}0.057 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$133 \mathrm{ms} \pm 471 \mathrm{μs}\left({\color{gray}-3.902 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$106 \mathrm{ms} \pm 455 \mathrm{μs}\left({\color{lightgreen}-5.481 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$132 \mathrm{ms} \pm 599 \mathrm{μs}\left({\color{gray}-0.340 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$113 \mathrm{ms} \pm 484 \mathrm{μs}\left({\color{gray}-4.684 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$121 \mathrm{ms} \pm 940 \mathrm{μs}\left({\color{gray}-3.671 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$121 \mathrm{ms} \pm 575 \mathrm{μs}\left({\color{lightgreen}-5.127 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$121 \mathrm{ms} \pm 500 \mathrm{μs}\left({\color{gray}-0.686 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$140 \mathrm{ms} \pm 747 \mathrm{μs}\left({\color{gray}4.28 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$149 \mathrm{ms} \pm 603 \mathrm{μs}\left({\color{gray}2.32 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$109 \mathrm{ms} \pm 619 \mathrm{μs}\left({\color{gray}4.28 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$601 \mathrm{ms} \pm 3.22 \mathrm{ms}\left({\color{gray}3.75 \mathrm{\%}}\right) $$	Flame Graph

[cursor]

PR Summary

Medium Risk
Changes the core data-dependency resolution logic for Param nodes (including cycle handling and projection-aware consensus), which can affect downstream optimization/analysis correctness. Risk is mitigated by several new targeted tests and updated snapshots covering mixed constants, projections, and cycles.

Overview
Unifies block-parameter (Param) resolution in data-dependency analysis so predecessors coming from graph edges and constant bindings are processed together, and removes the separate resolve_params_const path.

The new consensus check is projection-aware (resolves each predecessor through the full projection suffix before comparing) and cycle-aware (filters cyclic Backtrack predecessors, using non-cyclic init edges when present, and propagating Backtrack appropriately).

Adds new regression tests + UI snapshots for mixed constant/place predecessors and for several cycle scenarios (including loop-invariant projected fields), and extends InstSimplify with the integer rule 0 & x => 0. Also enables the nightly iterator_try_reduce feature to support the new reduction logic.

^{Reviewed by Cursor Bugbot for commit 4f7a295. Bugbot is set up for automated code reviews on this repo. Configure here.}

[augmentcode]

Review completed. No suggestions at this time.

Comment augment review to trigger a new review at any time.

[cursor]

Feature flag placed out of alphabetical order

Low Severity

The iterator_try_reduce feature flag is placed at the end of the library features list, breaking the otherwise alphabetical ordering. It belongs between iter_collect_into and likely_unlikely. Notably, this same PR correctly moved maybe_uninit_uninit_array_transpose into its proper alphabetical position, so the out-of-order placement of the new feature appears to be an oversight.

 

^{Reviewed by Cursor Bugbot for commit d93d11b. Configure here.}

[cursor]

Cursor Bugbot has reviewed your changes and found 1 potential issue.

There are 2 total unresolved issues (including 1 from previous review).

^{❌ Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in the Cursor dashboard.}

^{Reviewed by Cursor Bugbot for commit 4f7a295. Configure here.}

[cursor]

All-cyclic preds return Incomplete

Medium Severity

After filtering Backtrack predecessors, an empty candidate list makes try_reduce return None, which is handled like disagreement and yields Incomplete. Nested params whose predecessors are entirely cyclic should propagate Backtrack (as before) so outer φ-nodes can ignore that edge and still reach consensus with constants or other branches.

 

^{Reviewed by Cursor Bugbot for commit 4f7a295. Configure here.}