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  "id": "ARC-RN-003",
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  "title": "Machine Evidence Intake and Representation Fidelity: Methods Dossier",
  "short_title": "Machine Evidence Intake and Representation Fidelity",
  "abstract": "This methods dossier examines how language models intake, represent, retrieve, and transmit supplied evidence. It finds no universally best serialization: performance depends on model, task, evidence budget, context position, and what information must survive. Existing research provides useful tools for measuring factual recovery and citation behavior, but direct evidence remains limited for preserving epistemic status, provenance, uncertainty, scope, and relationships—especially across heterogeneous agents and repeated transmission. The dossier proposes a benchmark built around hidden canonical evidence graphs, controlled and native-format renderings, multiple models and context budgets, position variation, counterfactual contamination checks, and separate measures for recovery, attribution, uncertainty, omission, unsupported inference, and recursive survival. It is a literature-grounded methods proposal, not a new controlled experiment; its claims and proposed metrics require empirical validation.",
  "summary": "A literature-grounded methods dossier on evidence representation in language models and a proposed benchmark for fidelity across formats, models, context positions, and recursive transmission.",
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  "resource_status": "current",
  "publication_state": "published",
  "published_at": "2026-09-24T21:31:42Z",
  "content_updated_at": "2026-09-29T00:22:33Z",
  "tested_environments": [],
  "limitations": [
    "This is a research synthesis and methods proposal, not a newly conducted controlled experiment.",
    "The immutable supplied report retains its original session-local citation markers. A separate edited edition maps the supplied claim ledger to stable sources, records source-check scope, and corrects identified version-sensitive details; it is not a full independent replication or peer review.",
    "Actual author attribution is not established by the supplied artifact. OpenAI Deep Research is recorded as tool/provider attribution, not as proof of authorship. No independent reviewer is identified.",
    "Token counts are approximate and tokenizer-dependent."
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      "id": "abstract",
      "title": "How evidence is represented can shape what survives.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#abstract"
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      "id": "central-finding",
      "title": "Representation quality is conditional, not a single ranking.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#central-finding"
    },
    {
      "id": "evidence-and-gaps",
      "title": "Measure what the receiving system can recover—and what it changes.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#evidence-and-gaps"
    },
    {
      "id": "benchmark",
      "title": "Use a hidden evidence graph and test several dimensions independently.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#benchmark"
    },
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      "id": "interpretation",
      "title": "Treat the dossier as a measurement agenda.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#interpretation"
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      "id": "limitations",
      "title": "Scope and provenance matter.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#limitations"
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      "id": "full-report",
      "title": "Original and evidence-linked editions",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#full-report"
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