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提出证据状态校准（evidence-state calibration）概念与SuffiCE方法，通过微调模型下一token分布中的置信度，使其随证据变化而准确反映答对概率，在临床诊断和多跳问答上将Brier分数降低最高60%、AUROC提升最高0.16。
The article distinguishes between permission for AI agents to act and the evidence required to justify action, introducing the Organic Intelligence Protocol (OIP) to establish evidence boundaries, illustrated by a recent audit case study.
The paper introduces ClaimReceipt, a claim-relative receipt specification and verifier for verifying evidence sufficiency and coverage in agent evaluations, validated through experiments on historical records and prospective audits.
This paper proposes Evidence Sufficiency Boundary Training to enhance selective answering in grounded multi-hop QA systems by learning when to abstain from answering due to insufficient evidence, leading to improved boundary localization and reduced unsupported-answer rates on benchmarks like HotpotQA and MuSiQue.
This paper identifies 'context dilution' as a key failure mode in language agents performing multi-hop reasoning and introduces SLEUTH, an epistemic working memory that explicitly tracks confirmed facts, active hypotheses, and open questions, showing consistent gains across benchmarks.