Grading the Narrators: An Isnad-Rijal Framework for Claim-Level Provenance in Multi-Agent Knowledge Systems

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provenance multi-agent-systems knowledge-systems claim-level hadith-science evaluation

Summary

This paper proposes a framework based on Islamic hadith science for evaluating claim-level provenance in multi-agent knowledge systems, including a formal mapping, relational schema, decision matrix, and evaluation on 20,000 physics textbook claims.

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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Paper page - Grading the Narrators: An Isnad-Rijal Framework for Claim-Level Provenance in Multi-Agent Knowledge Systems

Source: https://huggingface.co/papers/2607.24117

Abstract

Modernmulti-agentknowledgesystemsincreasinglyaccumulateknowledgethroughchainsofautonomoustransformationsratherthandirectretrieval.Existingprovenanceworkrecordswhathappened-executiontraces,toolcalls,evidencelinks-andsource-reliabilityestimationislongestablished(truthdiscovery,reputationsystems).Whatismissingisanoperationalframeworkthatattachesgraded,per-domaintransmitterreliabilitytoclaim-leveltransmissionchains,withcompletenesssemantics,transformation-typedaggregation,decoupledcontentcriticism,andserve/review/quarantinerouting.ClassicalIslamichadithscienceconfrontedastructurallysimilarproblem:decidingwhetherknowledgetransmittedthroughchainsofhumannarratorsshouldbeaccepted.Overcenturiesitdevelopedarigorousmethodology-isnad(acompletetransmissionchainattachedtoeveryclaim),rijal(systematicgradingofeachnarrator’sintegrityandprecision),weakest-linkchainevaluation,corroborationthroughindependentchains,andmatncriticism(contentevaluatedindependentlyofchainquality).ThispapertransfersthatmethodologytoAIsystemdesign.Wecontributeaformalmappingfromhadith-scienceconceptstomulti-agentpipelines,arelationalschemaimplementingclaimchainsandagradednarratorregistry,adecisionmatrixcombiningchaingradewithcontentcriticism,andanevaluationon20,000claimsfromrealphysicstextbooks.Theevaluationvalidatesweakest-linkquarantineandindependent-chaincorroboration;reportsapartialfailureofthegrade-recoveryloop,whichmissedthehighest-faultnarrator;andreportstwoanalysesasinconclusive,includingamatched-coveragecomparisontheframeworkcouldnotreachwiththereferencecontentcritic.Thepaperisexplicitthroughoutaboutwhichclaimstheevidencedoesanddoesnotyetsupport.

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