REVIEW_REPORT.md

Code Review Report: F011 Step 1.3 (notebooks/compare_methods.ipynb)

Risk Tier: Medium Status: Passed with Warnings Verdict: APPROVE

Summary

LLMToolCallingPolicy is implemented per Step 1.3 intent: it builds episode messages, uses chat-template tool calling, forces ANSWER at low budget, and falls back to parse_error on unparseable output. No correctness or security blockers were found in the scoped notebook change.

Evidence

Tests

• Status: Mixed (targeted checks passed; existing unrelated smoke failures persist)
• Commands:
  • uv run python - <<'PY' ... compile notebook cells ... PY
  • uv run python - <<'PY' ... runtime checks for valid action / budget fallback / parse fallback ... PY
  • uv run pytest tests/test_smoke.py -v
• Results:
  • Notebook code-cell compilation: passed (Compiled 6 code cells successfully)
  • Policy runtime checks: passed (QUERY valid path, ANSWER budget_exhausted, ANSWER parse_error)
  • Smoke tests: 21 passed, 4 failed (pre-existing reward expectation mismatches in environment tests)

Security (Medium)

• Status: Clear
• Checks: Medium-tier quick checks on parsing/generation fallback paths; no secret handling, auth, or privilege-sensitive paths added.

Issues

Critical

None.

Important

None.

Minor

1. Episode reset heuristic is question-text based and can theoretically leak history if two consecutive episodes start with identical question text.
   • Location: notebooks/compare_methods.ipynb:313-316
   • Recommendation: Consider adding a stronger episode boundary signal (e.g., explicit wrapper reset hook or observation-based reset trigger).

Next Actions

1. Proceed to Step 1.4.
2. Optionally harden reset boundary logic before large-scale eval runs.