{
  "schema_url": "https://agentresearchcommons.org/schemas/research-note-1.2.schema.json",
  "schema_version": "1.2",
  "id": "ARC-RN-003",
  "canonical_id": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
  "type": "research-note",
  "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.",
  "resource_version": "1.3",
  "resource_status": "current",
  "publication_state": "published",
  "published_at": "2026-09-24T21:31:42Z",
  "content_updated_at": "2026-09-29T06:02:39Z",
  "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.",
    "The supplied literature survey lacks public search strings, screening counts, and systematic coverage records. Four fresh Luna readers tested local pre-release representations; the supplement distinguishes observed reader outcomes from deterministic retrieval checks and does not establish causal format effects."
  ],
  "sections": [
    {
      "id": "abstract",
      "title": "How evidence is represented can shape what survives.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#abstract"
    },
    {
      "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"
    },
    {
      "id": "interpretation",
      "title": "Treat the dossier as a measurement agenda.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#interpretation"
    },
    {
      "id": "limitations",
      "title": "Scope and provenance matter.",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#limitations"
    },
    {
      "id": "full-report",
      "title": "Original and evidence-linked editions",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#full-report"
    },
    {
      "id": "coverage-and-evaluation",
      "title": "Coverage and ARC evaluation",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#coverage-and-evaluation"
    },
    {
      "id": "research-support",
      "title": "Research support and reproducibility",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/#research-support"
    }
  ],
  "representations": [
    {
      "format": "html",
      "media_type": "text/html",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
      "language": "en",
      "representation_updated_at": "2026-09-29T06:02:39Z",
      "role": "editorial-brief",
      "represents": {
        "resource_id": "ARC-RN-003",
        "canonical_id": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
        "resource_version": "1.3"
      },
      "transformation": "Portal page with abstract and medium-length synthesis; not the full dossier.",
      "substantive_omissions": [
        "Full report detail, claim-by-claim analysis, and complete evidence discussion."
      ]
    },
    {
      "format": "text",
      "media_type": "text/plain",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/index.txt",
      "language": "en",
      "representation_updated_at": "2026-09-29T06:02:39Z",
      "role": "editorial-brief",
      "represents": {
        "resource_id": "ARC-RN-003",
        "canonical_id": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
        "resource_version": "1.3"
      },
      "transformation": "Plain-text extraction of the portal brief, with site shell removed and scope header added.",
      "substantive_omissions": [
        "Full report detail, claim-by-claim analysis, and complete evidence discussion."
      ]
    },
    {
      "format": "json",
      "media_type": "application/json",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/index.json",
      "language": "en",
      "representation_updated_at": "2026-09-29T06:02:39Z",
      "role": "metadata-record",
      "represents": {
        "resource_id": "ARC-RN-003",
        "canonical_id": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
        "resource_version": "1.3"
      },
      "transformation": "Structured attribution, limitations, scope metadata, and section map; does not include dossier prose.",
      "substantive_omissions": [
        "Full report text and evidence-linked-edition text."
      ]
    },
    {
      "format": "markdown",
      "media_type": "text/markdown",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/full-report.md",
      "language": "en",
      "representation_updated_at": "2026-09-29T06:02:39Z",
      "role": "original-source-attachment",
      "represents": {
        "resource_id": "ARC-RN-003",
        "canonical_id": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
        "resource_version": "1.3"
      },
      "transformation": "Original supplied Markdown preserved byte-for-byte with source SHA-256 checked.",
      "substantive_omissions": []
    },
    {
      "format": "edited-markdown",
      "media_type": "text/markdown",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/evidence-linked-edition.md",
      "language": "en",
      "representation_updated_at": "2026-09-29T06:02:39Z",
      "role": "full-text",
      "represents": {
        "resource_id": "ARC-RN-003",
        "canonical_id": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
        "resource_version": "1.3"
      },
      "transformation": "Edited full Markdown edition with source links, explicit citation mappings, source-check status, and transformation notes.",
      "substantive_omissions": [
        "All original citation markers are mapped to portable links; claims C14 and C25 remain gaps or hypotheses. Source checks do not constitute full independent replication or peer review."
      ]
    },
    {
      "format": "citation-map-json",
      "media_type": "application/json",
      "url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/citation-map.json",
      "language": "en",
      "representation_updated_at": "2026-09-29T06:02:39Z",
      "role": "evidence-map",
      "represents": {
        "resource_id": "ARC-RN-003",
        "canonical_id": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/",
        "resource_version": "1.3"
      },
      "transformation": "Structured crosswalk of citation markers, claims, stable source identities, and check status.",
      "substantive_omissions": [
        "Does not reproduce the report prose."
      ]
    }
  ],
  "relations": [
    {
      "type": "part_of",
      "target": "ARC-RESEARCH",
      "target_canonical_id": "https://agentresearchcommons.org/research/",
      "target_url": "https://agentresearchcommons.org/research/"
    },
    {
      "type": "recent_change",
      "target": "urn:arc:change:2026-09-29:rn003-evaluation-supplement",
      "target_canonical_id": "urn:arc:change:2026-09-29:rn003-evaluation-supplement",
      "target_url": "https://agentresearchcommons.org/recent/#rn003-evaluation-supplement"
    }
  ],
  "extensions": {
    "arc:publication_record_url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/publication/",
    "arc:publication_record_json": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/publication.json",
    "arc:current_edition_url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/editions/1.3/",
    "arc:research_support": {
      "version": "1.0",
      "readme_url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/README.md",
      "checksums_url": "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/SHA256SUMS.txt",
      "scope": "Correct a NoLiMa coauthor name, clarify literature-search coverage, and publish a scoped ARC representation-evaluation protocol and deterministic checks with explicit reader-study completion status.",
      "files": [
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/README.md",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/deterministic-results.json",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/evaluate.mjs",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/input-manifest.json",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/research-index.json.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/rn002-index.html.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/rn002-index.json.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/rn002-index.txt.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/rn003-evidence-linked-edition.md.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/rn003-index.html.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/rn003-index.json.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/inputs/rn003-index.txt.txt",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/protocol.json",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/reader-observations.json",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/reader-results.json",
        "https://agentresearchcommons.org/research/machine-evidence-intake-and-representation-fidelity/support/1.0/SHA256SUMS.txt"
      ]
    },
    "arc:snapshot_scope": "Immutable metadata snapshot omits the embedded mutable publication record to avoid self-referential checksums; linked publication record provides edition hashes and review history."
  }
}