用伊斯兰圣训学方法给AI知识传递链打分，提升可信度判断。
Grading the Narrators: An Isnad-Rijal Framework for Claim-Level Provenance in Multi-Agent Knowledge Systems
- 借鉴圣训学的传述链与人物信誉评估体系，构建可评分的知识传递框架。
- 在2万条物理课本命题上验证，弱链接隔离和独立链互证有效。
- 适合关注AI知识可信度、可解释性的研究者与系统设计者。
现代多智能体知识系统越来越多地通过自主转换链积累知识，而非直接检索。现有溯源工作记录事件过程（执行轨迹、工具调用、证据链接），来源可靠性评估也已有成熟方法（真相发现、声誉系统）。但缺乏一个能为每条主张的传输链赋予分级、领域特定的传述人可信度的实用框架，该框架需具备完整性语义、类型化变换聚合、内容批评与服务/审查/隔离路由分离等特性。古典伊斯兰圣训科学曾面临类似问题：如何判断通过人类传述链传递的知识是否可信。历经数百年发展出严谨方法——传述链（isnad，每个主张附带完整传输链）、人物评级（rijal，系统性评估每位传述人诚信与精确度）、最弱环节评估、独立链交叉验证，以及对内容本身（matn）的独立批判。本文将这一方法论迁移至AI系统设计。我们贡献了：将圣训科学概念形式化映射到多智能体流水线的方法；实现主张链与分级传述人注册表的关系模式；结合链质量与内容批判的决策矩阵；在2万条真实物理教科书命题上的评估。结果验证了最弱链隔离与独立链交叉验证的有效性；报告了评级恢复环的部分失败（未能识别最高故障传述人）；两项分析结果不明确，包括框架无法与基准内容批评达到匹配覆盖率的比较。论文始终明确指出哪些主张有证据支持，哪些尚未支持。
原文摘要 · Abstract (English)
Modern multi-agent knowledge systems increasingly accumulate knowledge through chains of autonomous transformations rather than direct retrieval. Existing provenance work records what happened - execution traces, tool calls, evidence links - and source-reliability estimation is long established (truth discovery, reputation systems). What is missing is an operational framework that attaches graded, per-domain transmitter reliability to claim-level transmission chains, with completeness semantics, transformation-typed aggregation, decoupled content criticism, and serve/review/quarantine routing. Classical Islamic hadith science confronted a structurally similar problem: deciding whether knowledge transmitted through chains of human narrators should be accepted. Over centuries it developed a rigorous methodology - isnad (a complete transmission chain attached to every claim), rijal (systematic grading of each narrator's integrity and precision), weakest-link chain evaluation, corroboration through independent chains, and matn criticism (content evaluated independently of chain quality). This paper transfers that methodology to AI system design. We contribute a formal mapping from hadith-science concepts to multi-agent pipelines, a relational schema implementing claim chains and a graded narrator registry, a decision matrix combining chain grade with content criticism, and an evaluation on 20,000 claims from real physics textbooks. The evaluation validates weakest-link quarantine and independent-chain corroboration; reports a partial failure of the grade-recovery loop, which missed the highest-fault narrator; and reports two analyses as inconclusive, including a matched-coverage comparison the framework could not reach with the reference content critic. The paper is explicit throughout about which claims the evidence does and does not yet support.
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