diff --git a/MaxKernel/evaluation/code_adapter/code_adapter.py b/MaxKernel/evaluation/code_adapter/code_adapter.py
index 42ea2ddc..27b84acc 100644
--- a/MaxKernel/evaluation/code_adapter/code_adapter.py
+++ b/MaxKernel/evaluation/code_adapter/code_adapter.py
@@ -1,3 +1,4 @@
+import ast
 import logging
 import time
 from typing import List, Optional, Union
@@ -11,12 +12,20 @@
 from evaluation.custom_types.kernel_task import KernelTask
 from hitl_agent.constants import MODEL_NAME
 
+# Adaptation reproduces the whole input script, so the response is long. Ask
+# for the model's full output budget rather than relying on the default.
+MAX_OUTPUT_TOKENS = 65536
+
 logging.basicConfig(
   level=logging.INFO,
   format="%(asctime)s [%(levelname)s] - %(message)s",
 )
 
 
+class OutputTruncatedError(Exception):
+  """The model ran out of output budget before finishing the script."""
+
+
 class CodeAdapter:
   """
   Uses an LLM to refactor raw Python/JAX code into a structured format
@@ -55,13 +64,16 @@ def adapt(
         )
       prompt = self._get_adapt_optimized_prompt(original_code, get_inputs_code)
 
-    config = genai.types.GenerateContentConfig(temperature=0.1)
+    config = genai.types.GenerateContentConfig(
+      temperature=0.1, max_output_tokens=MAX_OUTPUT_TOKENS
+    )
     attempt = 0
     while attempt < self.max_retries:
       try:
         response = self.client.models.generate_content(
           model=MODEL_NAME, contents=prompt, config=config
         )
+        self._check_finish_reason(response)
         code = response.text.strip()
         if code.startswith("```python"):
           code = code[len("```python") :].strip()
@@ -78,7 +90,21 @@ def adapt(
         ):
           raise ValueError("LLM output did not contain the required sections.")
 
+        # The section headers all appear near the top of the file, so they are
+        # still present when a long response is cut short. Parse the result to
+        # catch truncation before it reaches disk and fails inside the harness.
+        try:
+          ast.parse(code)
+        except SyntaxError as e:
+          raise ValueError(
+            f"LLM output is not valid Python (line {e.lineno}: {e.msg}). "
+            "The response was most likely truncated."
+          ) from e
+
         return code
+      except OutputTruncatedError:
+        # Deterministic: a retry produces the same overlong response.
+        raise
       except Exception as e:
         attempt += 1
         wait_time = 2**attempt
@@ -92,6 +118,26 @@ def adapt(
       f"Failed to refactor code after {self.max_retries} retries."
     )
 
+  def _check_finish_reason(self, response) -> None:
+    """Raises if the model stopped for any reason other than finishing."""
+    candidates = getattr(response, "candidates", None)
+    if not candidates:
+      raise ValueError("LLM returned no candidates.")
+
+    reason = getattr(candidates[0], "finish_reason", None)
+    if reason is None or reason == genai.types.FinishReason.STOP:
+      return
+
+    if reason == genai.types.FinishReason.MAX_TOKENS:
+      usage = getattr(response, "usage_metadata", None)
+      raise OutputTruncatedError(
+        f"LLM hit the {MAX_OUTPUT_TOKENS}-token output limit, so the "
+        f"refactored code is truncated. The input script is too large to "
+        f"adapt in one response. (usage: {usage})"
+      )
+
+    raise ValueError(f"LLM stopped early with finish_reason={reason}.")
+
   def generate_kernel_task(
     self,
     task_id: str,
diff --git a/MaxKernel/evaluation/harness_code.py b/MaxKernel/evaluation/harness_code.py
index e8f17b02..6ade8a5a 100644
--- a/MaxKernel/evaluation/harness_code.py
+++ b/MaxKernel/evaluation/harness_code.py
@@ -5,9 +5,11 @@
 import json
 import jax
 import jax.numpy as jnp
+import numpy as np
 import importlib
 import importlib.util
 import os
+import sys
 import traceback
 import xprof_utils
 
@@ -17,7 +19,16 @@ def load_module_from_path(module_name, file_path):
   if spec is None or spec.loader is None:
     raise ImportError(f"Could not load {module_name} from {file_path}")
   module = importlib.util.module_from_spec(spec)
-  spec.loader.exec_module(module)
+  # Register before exec_module: modules using `from __future__ import
+  # annotations` turn every annotation into a string, and dataclasses then
+  # resolves them via sys.modules[cls.__module__], which raises
+  # AttributeError on None if the module was never registered.
+  sys.modules[module_name] = module
+  try:
+    spec.loader.exec_module(module)
+  except BaseException:
+    sys.modules.pop(module_name, None)
+    raise
   return module
 
 
@@ -98,6 +109,44 @@ def run_xprof():
   return avg_wall_time, xprof_time
 
 
+def diff_metrics(b, o, chunk_elems=1 << 24):
+  \"\"\"Max absolute and max relative difference between two outputs.
+
+  Computed on the host in bounded-size chunks. `b` and `o` have already been
+  pulled off the device by `jax.device_get`, so the naive
+  `jnp.max(jnp.abs((b - o) / b))` ships them straight back: the true division
+  promotes to float, and for a large integer output (e.g. a 4 GiB uint8 paged
+  KV cache) that is a 16 GiB argument plus a 16 GiB result, which does not fit
+  in HBM. These are diagnostic metrics only -- `is_correct` comes from
+  `jnp.allclose` -- but raising here used to fail the whole case.
+
+  Chunking also fixes two latent issues with the old expression: the
+  subtraction no longer wraps around for unsigned dtypes, and a NaN in one
+  chunk no longer suppresses the maximum found in the others.
+  \"\"\"
+  fb = np.asarray(b).reshape(-1)
+  fo = np.asarray(o).reshape(-1)
+  max_abs = 0.0
+  max_rel = 0.0
+  for i in range(0, fb.size, chunk_elems):
+    x = fb[i:i + chunk_elems].astype(np.float64)
+    y = fo[i:i + chunk_elems].astype(np.float64)
+    d = np.abs(x - y)
+    # max() over an empty slice is undefined; size is never 0 here but guard
+    # anyway so a zero-sized output cannot take down the comparison.
+    if d.size == 0:
+      continue
+    max_abs = max(max_abs, float(np.max(d)))
+    with np.errstate(divide="ignore", invalid="ignore"):
+      r = d / np.abs(x)
+    # Matches the previous definition |(b - o) / b|: division by a zero
+    # reference stays +inf, and 0/0 stays NaN rather than being counted.
+    r = r[~np.isnan(r)]
+    if r.size:
+      max_rel = max(max_rel, float(np.max(r)))
+  return max_abs, max_rel
+
+
 def main():
   try:
     # Load task configuration from task.json
@@ -266,8 +315,9 @@ def main():
           if b.shape != o.shape:
              raise ValueError(f"Shape mismatch: {b.shape} vs {o.shape}")
           is_correct = is_correct and bool(jnp.allclose(b, o, atol=curr_atol, rtol=curr_rtol))
-          max_abs_diff = max(max_abs_diff, float(jnp.max(jnp.abs(b - o))))
-          max_rel_diff = max(max_rel_diff, float(jnp.max(jnp.abs((b - o) / b))))
+          leaf_abs, leaf_rel = diff_metrics(b, o)
+          max_abs_diff = max(max_abs_diff, leaf_abs)
+          max_rel_diff = max(max_rel_diff, leaf_rel)
       except Exception as e:
         harness_logs.append(f"Correctness check failed for input {idx}: {e}")
         is_correct = False