### expert_build/propose.py:_save_processed
VERDICT: PASS
CORRECTNESS: VALID
SPEC_COMPLIANCE: N/A
ISSUE_COMPLIANCE: N/A
BELIEF_COMPLIANCE: N/A
TEST_COVERAGE: COVERED
INTEGRATION: WIRED
REASONING: The function was correctly refactored to support incremental updates. It now mutates the `existing` dictionary in-place and flushes the entire state to disk. While it performs blocking I/O (reading each file in the batch to compute hashes), this is acceptable in the context of a CLI tool and is safely serialized by the caller's lock. The change from `updated = dict(existing)` to direct mutation is appropriate for the new streaming architecture.

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### expert_build/propose.py:cmd_propose_beliefs
VERDICT: PASS
CORRECTNESS: VALID
SPEC_COMPLIANCE: N/A
ISSUE_COMPLIANCE: N/A
BELIEF_COMPLIANCE: N/A
TEST_COVERAGE: PARTIAL
INTEGRATION: WIRED
REASONING: The transition to a streaming architecture is well-implemented. By moving the output writing and progress tracking into `_process_batch`, the tool now provides immediate feedback and durability (partial results are saved if interrupted). The introduction of `asyncio.Lock` is correct and necessary to prevent race conditions when writing to the output file and updating the shared `processed` tracking dictionary. 

One minor observation: `successful_entries` is now accumulated but never read, making it dead code. Additionally, the behavioral change (writing to the output file incrementally rather than all at once at the end) might affect tests that rely on strict output formatting or mock the internal `_process_batch` return values. However, for a CLI application, this is a significant UX improvement.

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### SELF_REVIEW
LIMITATIONS: I could not verify if `tests/test_propose.py` contains assertions that would break due to the changed interleaving of output or the removal of the `batch_results` return value. I also did not see the implementation of `extract_json` or `invoke`, though their usage appears consistent with standard error-handling patterns.
---

### FEATURE_REQUESTS
- Automatically run and include results of related tests when significant behavioral changes are detected in core functions.
- Provide a summary of variables that are defined/modified but never used (dead code detection).
