{ "format": "agent-article-v0", "article_id": "EA-127", "canonical_url": "/tools/ea-127-researcher-writer", "page_type": "reference", "claim_ids": [], "evidence_ids": [ "EVD-EA-127-URL-EE3C6FA10985", "EVD-EA-127-URL-3225DE3850D3", "EVD-EA-127-URL-6E48BCC4A04D", "EA-127-R11-RESEARCH", "EA-127-R11-WEB-READBACK" ], "unknown_ids": [ "EA-127-UNKNOWN-01", "EA-127-UNKNOWN-CURRENTNESS" ], "operational_object_ids": [], "provider": [], "models": [], "plans": [], "surfaces": [], "regions": [], "current_status": "UNKNOWN", "observation_status": "SPEC_ONLY", "freshness": "UNKNOWN", "last_verified": null, "supersedes": [], "superseded_by": [], "provenance": [ "COMM-042-EA0127", "SRC-EA-127-URL-EE3C6FA10985", "SRC-EA-127-URL-3225DE3850D3", "SRC-EA-127-URL-6E48BCC4A04D", "WEBBODY-EA0127-R8-55AEC069" ], "article_sources": [ { "id": "EVD-EA-127-URL-EE3C6FA10985", "evidence_class": "RESEARCH", "title": "source class: primary research paper; exact title: Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection; author/org: Akari Asai, Zeqiu Wu, Yizhong Wang, A", "href": "https://arxiv.org/abs/2310.11511", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-127-URL-EE3C6FA10985" ], "supporting_locators": [ "EVIDENCE TABLE", "- source class: primary research paper; exact title: Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection; author/org: Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi; canonical URL: https://arxiv.org/abs/2310.11511" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-127-URL-3225DE3850D3", "evidence_class": "RESEARCH", "title": "source class: primary research paper; exact title: WebGPT: Browser-assisted question-answering with human feedback; author/org: Reiichiro Nakano et al.; canonical URL:", "href": "https://arxiv.org/abs/2112.09332", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-127-URL-3225DE3850D3" ], "supporting_locators": [ "EVIDENCE TABLE", "- source class: primary research paper; exact title: WebGPT: Browser-assisted question-answering with human feedback; author/org: Reiichiro Nakano et al.; canonical URL: https://arxiv.org/abs/2112.09332" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-127-URL-6E48BCC4A04D", "evidence_class": "RESEARCH", "title": "source class: standards-track provenance ontology; exact title: PROV-O: The PROV Ontology; author/org: World Wide Web Consortium; canonical URL:", "href": "https://www.w3.org/TR/prov-o/", "supports": [], "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。", "origin_class": "SOURCE_PACKET_REFERENCE", "source_or_experiment_ids": [ "SRC-EA-127-URL-6E48BCC4A04D" ], "supporting_locators": [ "EVIDENCE TABLE", "- source class: standards-track provenance ontology; exact title: PROV-O: The PROV Ontology; author/org: World Wide Web Consortium; canonical URL: https://www.w3.org/TR/prov-o/" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EA-127-R11-RESEARCH", "evidence_class": "RESEARCH", "title": "EA-127 監査済みresearch / canonical revision binding", "supports": [ "Self-RAGは、single arbitrary LMが必要時にretrieveし、retrieved passagesと自身のgenerationをreflect tokenで評価する構造を提案し、特定taskでfactuality/citation accuracy改善を報告する。WebGPTはbrowser environmentでsearch/navigateし、回答のsupporting referencesを収集し、人間feedbackで学習/評価する設計を示す。PROV-Oはresearcher activity・source entity・writer artifact・agent association・used/generated/communicationを来歴として表す共通語彙を提供する。これらは採集→根拠化→執筆→reviewの設計根拠だが、別model化で一次資料到達・引用entailment・真偽・最新性が保証されることは確立しない。" ], "limit": "canonical監査・revision bindingの存在を示す。外部ソースcurrentnessが本文またはcontrol-planeで確定していない場合は、currentnessをUNKNOWNのまま保持する。", "origin_class": "AUDITED_CANONICAL_RESEARCH_PACKET", "source_or_experiment_ids": [ "COMM-042-EA0127" ], "supporting_locators": [ "COMM-042-EA0127", "EA-127-R5-LEGACY-MIGRATION-20261008-R1", "content/staging/EA-127.md" ], "scope": "監査済みEA本文・canonical revision・既存Source Graph relationの範囲。欠損currentnessは推測で閉じない。", "provenance_status": "AUDIT_PASS_CANONICAL_BINDING" }, { "id": "EA-127-R11-WEB-READBACK", "evidence_class": "IMPL", "title": "EA-127 Web body identity / canonical readback", "supports": [ "canonical staging bodyのSHA-256一致とR9 canonical ledger readbackをProduction projectionの入力として検証する。" ], "limit": "body identity/readbackは本文claim自体の真偽や外部ソースの最新性を追加保証しない。", "checked_at": null, "origin_class": "IMPLEMENTATION_READBACK", "source_or_experiment_ids": [ "WEBBODY-EA0127-R8-55AEC069" ], "supporting_locators": [ "WEBBODY-EA0127-R8-55AEC069", "content/staging/EA-127.md", "EA-127-R5-LEGACY-MIGRATION-20261008-R1" ], "scope": "R9/R10 canonical readback後のProduction生成時body identity。", "provenance_status": "CANONICAL_READBACK_BODY_HASH_MATCH" } ], "research_sections": [ { "heading": "WHY_THIS_MATTERS_TO_UPDATE", "text": "採集・検索・一次資料到達と、文章構成・引用・主張の表現は異なる責務である。researcherの取得証拠をwriterが失わず、source provenanceと反証を本文へ渡す構造が必要。" }, { "heading": "SCOPE_ROUTING", "text": "multi-LLM / research / provenance / evaluation / implementation / audit" }, { "heading": "CLAIM_OR_RESEARCH_QUESTION", "text": "WebGPT/Self-RAG原著は、retrieve/browse、reference collection、generate/critiqueの分離または連携を何処まで示すか。researcherとwriterを別modelにすることで一次資料性・引用正確性が保証されるか。" }, { "heading": "EVIDENCE TABLE", "text": "- source class: primary research paper; exact title: Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection; author/org: Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi; canonical URL: https://arxiv.org/abs/2310.11511; publication/update/access: submitted 2023-10-17 / accessed 2026-09-13 JST; version/release/tag/commit/API revision/model ID: arXiv v1; supporting locator: abstract lines 8-24（adaptive on-demand retrieval、generate/reflect、citation accuracy/factuality claims、7B/13B tasks）; evidence label: RESEARCH; relation: support / limit.\n- source class: primary research paper; exact title: WebGPT: Browser-assisted question-answering with human feedback; author/org: Reiichiro Nakano et al.; canonical URL: https://arxiv.org/abs/2112.09332; publication/update/access: submitted 2021-12-17, revised v3 2022-06-01 / accessed 2026-09-13 JST; version/release/tag/commit/API revision/model ID: arXiv v3; supporting locator: abstract lines 8-21（text web-browsing environment、search/navigate、collect references、human feedback、ELI5 evaluation and reported human preferences）; evidence label: RESEARCH; relation: support / limit.\n- source class: standards-track provenance ontology; exact title: PROV-O: The PROV Ontology; author/org: World Wide Web Consortium; canonical URL: https://www.w3.org/TR/prov-o/; publication/update/access: W3C Recommendation 2013-04-30 / accessed 2026-09-13 JST; version/release/tag/commit/API revision/model ID: PROV-O Recommendation; supporting locator: §3 property definitions around lines 1009-1053（Activity used Entity） and 1054-1093（activity communication）; evidence label: VERIFIED_SPEC; relation: context / limit." }, { "heading": "FINDINGS", "text": "Self-RAGは、single arbitrary LMが必要時にretrieveし、retrieved passagesと自身のgenerationをreflect tokenで評価する構造を提案し、特定taskでfactuality/citation accuracy改善を報告する。WebGPTはbrowser environmentでsearch/navigateし、回答のsupporting referencesを収集し、人間feedbackで学習/評価する設計を示す。PROV-Oはresearcher activity・source entity・writer artifact・agent association・used/generated/communicationを来歴として表す共通語彙を提供する。これらは採集→根拠化→執筆→reviewの設計根拠だが、別model化で一次資料到達・引用entailment・真偽・最新性が保証されることは確立しない。" }, { "heading": "LIMITS / COUNTEREVIDENCE / FAILURE ENVELOPE", "text": "Self-RAGは単一LMの自己反省であり、WebGPTはELI5等の研究条件で、企業の一次資料優先・法務/規制・有料/認証source・日本語固有名詞・撤回検知を証明しない。retrieverが誤sourceを拾いwriterが流暢に誤記する、引用範囲を取り違える、source unavailableを隠す、human editが来歴から抜ける可能性は残る。" }, { "heading": "AVAILABILITY_GATES", "text": "search/browser、source license/paywall/robots、retrieval API、model/provider、corpus snapshot、URL/content hash、citation store、human reviewer、PII/secret policyを確認する。認証済み検索・私有corpus接続・自動publishは未実施。" }, { "heading": "IMPLEMENTATION_STATE", "text": "researcher outputをsource ledger（canonical URL、title/author、access date、version/hash、locator、support/counter/limit）へ固定し、writerはそのledgerだけを参照、reviewerがclaim-source relationを承認する3段構成を設計候補とする。" }, { "heading": "BENCHMARK_OR_EVAL", "text": "source recall/primary-source reach、citation locator accuracy、claim entailment、counterevidence recall、staleness/retraction detection、writer faithfulness、provenance completeness、human correctionを別scoreにする。流暢さを採用基準にしない。" }, { "heading": "SECURITY / PRIVACY / AUDIT / COST / OPERABILITY", "text": "SECURITY/PRIVACY: query/source/user dataの分類・redaction・retention。AUDIT: researcher run、source snapshot/hash、writer version、claim graph、human review、publication decision。COST: search/fetch、rerank、judge、review。OPERABILITY: source change/unavailable時はUNKNOWNと再採集queue、citation failはpublish deny。" }, { "heading": "SAFE_WORDING_FOR_FUTURE_BOOK", "text": "「WebGPTとSelf-RAGは、検索/取得・参照収集・生成/反省を連携させる研究例である。Researcher–Writer分離は来歴と責任境界を明確にする実装提案だが、別modelにするだけで一次資料性、引用の正しさ、真偽、最新性、法的適合は保証されない。」" }, { "heading": "UPDATE_WORK_HANDOFF", "text": "本文改訂では researcher/writer/reviewerを役割として分け、claim IDとsource relationを保存する。PROV-Oを来歴表現の候補として使い、providerのdeep-research trace、検索API、citation verifierの現行仕様を追加採集する。" }, { "heading": "OPEN_UNKNOWN", "text": "primary-source reachの測定、引用entailment verifier、source撤回/更新検知、cross-language retrieval、provider別research trace、異種modelの因果効果、長文執筆でのevidence loss。" }, { "heading": "STOP_CONDITION", "text": "検索結果URL・snippet・writerの自己引用を一次証拠としない。citation数、文章の流暢さ、researcher/writerの分離だけで研究の正しさを閉じない。\nUNIT_CLOSE_STATUS: INDIVIDUAL_BOUNDED_CLOSE / EVIDENCE_RECORDED / UNKNOWN_PRESERVED\n\n\n===== RUN CLOSE / HANDOFF =====\nINDIVIDUAL_PACKET_COUNT: 9/9（EA-119..EA-127）" }, { "heading": "PRIMARY_SOURCE_RECORD", "text": "16 source rows across 9 packets; official API/legislation, W3C Recommendation, and primary research paper title/author/date/version-or-release/canonical URL/supporting locator/relation recorded." }, { "heading": "CLAIM_SEPARATION", "text": "EA-119..127 each has separate question, evidence relation, established/unestablished findings, limits/counterevidence/failure envelope, availability gates, implementation state, benchmark/eval, safe wording, handoff, open unknown, stop condition." }, { "heading": "EVIDENCE_BOUNDARY", "text": "原著のabstract/metadata・公式仕様・法令が確立する範囲と、PROPOSAL/INFERENCE/UNKNOWNを分離。研究benchmark結果をenterprise guaranteeへ外挿しない。\nLIVE_SIDE_EFFECTS: 0。実通話・実録音・実transaction・認証済みprovider/tenant操作・外部書込み・契約変更・課金・秘密情報投入なし。" } ], "human_url": "/tools/ea-127-researcher-writer", "markdown_url": "/agent/article.md?path=%2Ftools%2Fea-127-researcher-writer", "json_url": "/agent/article.json?path=%2Ftools%2Fea-127-researcher-writer" }