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Existing evaluations primarily measure final outputs, leaving unclear whether a model recognized unreliable evidence, identified the faulty source, or prioritized the appropriate artifact.\n  We introduce TRACE, a controlled method for evaluating how LLMs assess and prioritize conflicting software artifacts. TRACE constructs paired clean and perturbed versions of real-world Java method bundles by injecting known faults into the documentation, implementation, or both while holding the remaining artifacts fixed. Models then assess artifact quality, detect and localize inconsistencies, and rank the available sources by reliability. Using 22,339 valid responses from seven LLMs on 456 method bundles, we find that quality penalties are g","title":"Measuring LLM Trust Allocation Across Conflicting Software Artifacts","url":"https://arxiv.org/abs/2604.03447","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.03447v2 Announce Type: replace-cross \nAbstract: LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mutually inconsistent. Existing evaluations primarily measure final outputs, leaving unclear whether a model recognized unreliable evidence, identified the faulty source, or prioritized the appropriate artifact.\n  We introduce TRACE, a controlled method for evaluating how LLMs assess and prioritize conflicting software artifacts. 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