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Findings show that historical evidence use should be evaluated under state variation, rather than inferred from outcome-only correctness, and that open-weight models rely heavily on context continuity and are reluctant to spontaneously switch to tool-mediated retrieval when provenance fails.
CARE (Canonicalization, Attribution, and Resolution Engine), a shell-specific, static-first verifier for individual shell commands before execution can reduce dispatch-boundary risk for LLM agents while preserving most benign workflows.
Experiments across multiple long-context narrative question answering and claim verification settings show that ClueWeaver substantially improves local end-to-end language models while providing evidence coverage and paragraph-referenced reasoning traces.
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