{ "format": "agent-article-v0", "article_id": "EA-125", "canonical_url": "/tools/ea-125-generator-verifier", "page_type": "reference", "claim_ids": [], "evidence_ids": [ "EVD-EA-125-URL-EEA50D24E419", "EVD-EA-125-URL-16E11E26BE5B", "EA-125-R11-RESEARCH", "EA-125-R11-WEB-READBACK" ], "unknown_ids": [ "EA-125-UNKNOWN-01", "EA-125-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-EA0125", "SRC-EA-125-URL-EEA50D24E419", "SRC-EA-125-URL-16E11E26BE5B", "WEBBODY-EA0125-R8-B90F46EA" ], "article_sources": [ { "id": "EVD-EA-125-URL-EEA50D24E419", "evidence_class": "RESEARCH", "title": "source class: primary research paper; exact title: Let's Verify Step by Step; author/org: Hunter Lightman et al.; canonical URL:", "href": "https://arxiv.org/abs/2305.20050", "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-125-URL-EEA50D24E419" ], "supporting_locators": [ "EVIDENCE TABLE", "- source class: primary research paper; exact title: Let's Verify Step by Step; author/org: Hunter Lightman et al.; canonical URL: https://arxiv.org/abs/2305.20050" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EVD-EA-125-URL-16E11E26BE5B", "evidence_class": "RESEARCH", "title": "source class: primary research paper; exact title: Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena; author/org: Lianmin Zheng et al.; canonical URL:", "href": "https://arxiv.org/abs/2306.05685", "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-125-URL-16E11E26BE5B" ], "supporting_locators": [ "EVIDENCE TABLE", "- source class: primary research paper; exact title: Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena; author/org: Lianmin Zheng et al.; canonical URL: https://arxiv.org/abs/2306.05685" ], "scope": "EVIDENCE TABLE", "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED" }, { "id": "EA-125-R11-RESEARCH", "evidence_class": "RESEARCH", "title": "EA-125 監査済みresearch / canonical revision binding", "supports": [ "Let's Verify Step by Stepは、最終結果へのfeedbackと各intermediate reasoning stepへのprocess feedbackを比較し、論文のMATH条件ではprocess supervisionが優れ、代表subsetで78%を解いたと報告する。これは中間step検証の研究根拠である。ただし、generatorが作る任意のcode、メール、契約、tool actionを、別LLM verifierが安全に検証できることは確立しない。verifierが同じモデル知識/誤りを共有する場合、独立性は弱い。" ], "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-EA0125" ], "supporting_locators": [ "COMM-042-EA0125", "EA-125-R5-LEGACY-MIGRATION-20261008-R1", "content/staging/EA-125.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-125-R11-WEB-READBACK", "evidence_class": "IMPL", "title": "EA-125 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-EA0125-R8-B90F46EA" ], "supporting_locators": [ "WEBBODY-EA0125-R8-B90F46EA", "content/staging/EA-125.md", "EA-125-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": "作るモデルと検証するモデルを分け、最終結果だけでなく中間step・制約・証拠を検査する設計は、業務agentの誤りを早期停止する入口になる。" }, { "heading": "SCOPE_ROUTING", "text": "multi-LLM / evaluation / implementation / security / audit" }, { "heading": "CLAIM_OR_RESEARCH_QUESTION", "text": "process supervision研究は、outcome supervisionよりstep-level verificationが信頼性向上に有効と示すか。数学trainingの結果を一般agentのruntime verifierへ適用できるか。" }, { "heading": "EVIDENCE TABLE", "text": "- source class: primary research paper; exact title: Let's Verify Step by Step; author/org: Hunter Lightman et al.; canonical URL: https://arxiv.org/abs/2305.20050; publication/update/access: submitted 2023-05-31 / accessed 2026-09-13 JST; version/release/tag/commit/API revision/model ID: arXiv v1; supporting locator: abstract lines 8-22（outcome vs process supervision、process supervisionがMATHでoutcome supervisionを上回るとの報告、MATH representative subset 78%、PRM800K release）; evidence label: RESEARCH; relation: support / limit.\n- source class: primary research paper; exact title: Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena; author/org: Lianmin Zheng et al.; canonical URL: https://arxiv.org/abs/2306.05685; publication/update/access: revised v4 2023-12-24 / accessed 2026-09-13 JST; version/release/tag/commit/API revision/model ID: arXiv v4; supporting locator: abstract lines 16-23（judge limitations and benchmarked human-preference agreement）; evidence label: RESEARCH; relation: context / limit." }, { "heading": "FINDINGS", "text": "Let's Verify Step by Stepは、最終結果へのfeedbackと各intermediate reasoning stepへのprocess feedbackを比較し、論文のMATH条件ではprocess supervisionが優れ、代表subsetで78%を解いたと報告する。これは中間step検証の研究根拠である。ただし、generatorが作る任意のcode、メール、契約、tool actionを、別LLM verifierが安全に検証できることは確立しない。verifierが同じモデル知識/誤りを共有する場合、独立性は弱い。" }, { "heading": "LIMITS / COUNTEREVIDENCE / FAILURE ENVELOPE", "text": "MATH training・reward model・step labelsという設定であり、enterprise runtimeのdeterministic verifierではない。process traceが真のchain-of-thoughtか、検証可能な外部stateかを分ける必要がある。verifierのfalse negative/positive、spec incompleteness、hidden side effect、long-horizon state driftは未確立。" }, { "heading": "AVAILABILITY_GATES", "text": "generator/verifier model/API/license、trace exposure、tool/state snapshot、rubric/spec、human escalation、privacy、latency/costを確認する。実generator/verifier run未実施。" }, { "heading": "IMPLEMENTATION_STATE", "text": "typed output→schema/policy/static check→independent verifier→human gate→commit/actionの多段制御を設計候補。verifier passを実行権限へ直結しない。" }, { "heading": "BENCHMARK_OR_EVAL", "text": "final outcome accuracyとstep-level validity、constraint violations、evidence entailment、verifier precision/recall、false accept/reject、time/cost、adversarial perturbationを分けて測る。" }, { "heading": "SECURITY / PRIVACY / AUDIT / COST / OPERABILITY", "text": "SECURITY/PRIVACY: verifierへ必要最小のredacted trace、untrusted output隔離。AUDIT: generator/verifier IDs、spec/rubric、step hash、decision、human override。COST: every-step verification、retry、human review。OPERABILITY: fail closed、unknown queue、verifier unavailable時のdeny。" }, { "heading": "SAFE_WORDING_FOR_FUTURE_BOOK", "text": "「Process supervision研究は、MATHの特定条件で中間stepへの検証が有効であると報告する。Generator–Verifierはruntime safetyの設計候補だが、verifier passは任意業務の正しさ・副作用なし・法的適合を保証せず、specと独立評価が必要である。」" }, { "heading": "UPDATE_WORK_HANDOFF", "text": "本文では outcome/process、generator/verifier、artifact/state、static/human checkを分離する。code・document・transactionごとに検証可能なspecを作り、研究値を業務保証に変換しない。" }, { "heading": "OPEN_UNKNOWN", "text": "runtime process verifierの有効性、verifier independence、tool side-effect検出、spec coverage、adversarial evasion、cost/latency frontier。" }, { "heading": "STOP_CONDITION", "text": "verifierが「OK」と出したこと、stepが流暢なこと、最終結果が一度合ったことを安全完了の証拠としない。\nUNIT_CLOSE_STATUS: INDIVIDUAL_BOUNDED_CLOSE / EVIDENCE_RECORDED / UNKNOWN_PRESERVED\n\n---" } ], "human_url": "/tools/ea-125-generator-verifier", "markdown_url": "/agent/article.md?path=%2Ftools%2Fea-125-generator-verifier", "json_url": "/agent/article.json?path=%2Ftools%2Fea-125-generator-verifier" }