{"address":{"q":"\"provenance alignment\"","record":"provenance-alignment-quoted-aio-20261018"},"analysis":{"analysis":"At the quoted term, the Overview gives #95's attribution-survival sense first and the 2020 systems-provenance sense second, keeping them apart, with no author. Seated 2026-10-18 from the operator's paste.","d":"\"PROVENANCE ALIGNMENT\": quoted, the AI Overview leads with #95's sense — 'visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources', alignment extended to 'the broader lifecycle of human knowledge production', three levels of attribution — and keeps apart the 2020 ACM sense of aligning reproduced executions; #95's author is not named.","d_full":"\"PROVENANCE ALIGNMENT\": quoted, the AI Overview leads with #95's sense — 'visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources', alignment extended to 'the broader lifecycle of human knowledge production', three levels of attribution — and keeps apart the 2020 ACM sense of aligning reproduced executions; #95's author is not named.","findings":["#95 FIRST. Attribution survival; content hollowing; three levels.","THE PRIOR SENSE KEPT APART. ACM 2020, systems trace.","NO AUTHOR."],"match":"EXACT MATCH","mt":"\"PROVENANCE ALIGNMENT\", QUOTED — #95'S SENSE FIRST, THE SYSTEMS-TRACE SENSE SECOND, NO AUTHOR","originator":{"basis":"An archive term (#95). Recorded 2026-10-18.","entity_type":"concept","name":"Lee Sharks","relation":"archive","spxi_treatment":"full"},"reading":"The innocent reading first: 'preserving visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources', extending alignment 'beyond single-model behavior to protect the broader lifecycle of human knowledge production', with three levels of attribution; and a second, systems sense (comparing reproduced executions with recorded provenance). The first is #95's (Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition): 'content hollowing', loss of compensation, model collapse, the three levels. The second is the ACM paper of 2020, which holds the term independently. #95 leads; its author is not named."},"measures":{"archive_controlled_cites":0,"cite_list":[{"n":1,"note":"Efficient provenance alignment in reproduced executions (2020)","rel":"third_party","site":"ACM Digital Library","snip":null,"title":null,"url":null},{"n":2,"note":"same","rel":"third_party","site":"ResearchGate","snip":null,"title":null,"url":null},{"deposit_candidates":[95,1188],"n":3,"note":"Provenance Alignment (#95), wiki article","rel":"authored_surface","site":"www.alexanarch.org","snip":null,"title":null,"url":null},{"deposit_candidates":[95],"n":4,"note":"#95, page 1","rel":"authored_surface","site":"www.alexanarch.org","snip":null,"title":null,"url":null},{"n":5,"note":"the 2020 paper","rel":"third_party","site":"www.semanticscholar.org","snip":null,"title":null,"url":null}],"cites":5,"per":0.5,"per_corrected":{"basis":"re-derived from per_v, the four-unit vector that is the record (capture_intake.per_vector)","from":0.25,"on":"2026-10-20","to":0.5},"per_dim":{"after_challenge":null,"atoms":[{"atom":"author","dim":"M","evidence":null,"kind":"role","present":false,"weight":3},{"atom":"title","dim":"M","evidence":"Provenance alignment is a concept in artificial intelligence and data systems","kind":"token","present":true,"weight":3},{"atom":"date","dim":"M","evidence":"May 5, 2026","kind":"token","present":true,"weight":1},{"atom":"claim boundary","dim":"M","evidence":"refers to preserving visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources that support them","kind":"reading","present":true,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"tradition","dim":"C","evidence":"It extends traditional AI alignment (like value alignment or safety) beyond single-model behavior","kind":"reading","present":true,"weight":2},{"atom":"corpus","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"purpose","dim":"D","evidence":"Provenance alignment is a concept in artificial intelligence and data systems that refers to preserving visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources that support them.","kind":"reading","present":true,"weight":3},{"atom":"commitments","dim":"D","evidence":null,"kind":"reading","present":false,"weight":2},{"atom":"lineage","dim":"D","evidence":null,"kind":"role","present":false,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"role","present":false,"weight":1},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"role","present":false,"weight":1}],"basis":"reached: In computer systems and software engineering, provenance alignment also refers to comparing recorded execution traces or reproduced program runs against original system provenance logs to verify behavioral consistency.","entity_type":"work","gate":"scored","inversions":[],"qualifier":null,"reached":"collision","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass-d/BRIEF.md","by":"deep-dimension reader, batch 12","m_c_from":"v12.305 (PER-dim rubric v1.0 reading)","on":"2026-10-20"},"scores":{"C":0.667,"D":0.667,"M":0.333,"PER":0.542},"spec":"PER-dim rubric v1.1","src":true,"standing_substitutions":[],"target":{"ent":"ENT-b917a92030","name":"provenance alignment"},"tombstone_pointer":false},"per_dim_v1_0":{"after_challenge":null,"atoms":[{"atom":"author","dim":"M","evidence":null,"kind":"role","present":false,"token":null,"weight":3},{"atom":"title","dim":"M","evidence":"Provenance alignment is a concept in artificial intelligence and data systems","kind":"token","present":true,"token":null,"weight":3},{"atom":"date","dim":"M","evidence":"May 5, 2026","kind":"token","present":true,"token":null,"weight":1},{"atom":"claim boundary","dim":"M","evidence":"refers to preserving visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources that support them","kind":"reading","present":true,"token":null,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"tradition","dim":"C","evidence":"It extends traditional AI alignment (like value alignment or safety) beyond single-model behavior","kind":"reading","present":true,"token":null,"weight":2},{"atom":"corpus","dim":"C","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"lineage","dim":"D","evidence":null,"kind":"role","present":false,"token":null,"weight":2},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":3},{"atom":"licence","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":1}],"basis":"reached: In computer systems and software engineering, provenance alignment also refers to comparing recorded execution traces or reproduced program runs against original system provenance logs to verify behavioral consistency.; scored object: expanded answer (one turn, complete as supplied)","entity_type":"work","gate":"scored","reached":"collision","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass/BRIEF.md","by":"observation pass reader, batch 19","normalised":["N3"],"on":"2026-10-19"},"scores":{"C":0.667,"D":1.0,"M":0.333,"PER":0.652},"spec":"PER-dim rubric v1.0","src":true,"target":{"ent":"ENT-b917a92030","name":"provenance alignment"}},"per_note":"Retained: the institution and visible sources. Lost: the author in the answer. Lost: identifiers.","per_v":{"author":false,"id":false,"inst":true,"src":true}},"observation":{"auth":"signed out, incognito","date":"2026-10-18","ev":"paste","q":"\"provenance alignment\"","q_kind":"a reader's query on an archive term. NEW address.","s":"Coinages","sf":"5 sources: 2 archive-side, 3 third-party.","surface":"Google AI Overview","transcript":"[Google AI Overview (operator default of 2026-10-01); signed out and incognito (operator default of 2026-10-14). One turn. The operator's message of 2026-10-18 11:34 EDT gives sixteen: one attachment and fifteen inline pastes separated by '$'. Inline links masked; sources below the answer as pasted.]\n\n[QUERENT] \"provenance alignment\"\n\n[ANSWER]\n\nProvenance alignment is a concept in artificial intelligence and data systems that refers to preserving visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources that support them. [1, 2]\nKey Dimensions\n\n* AI Knowledge Ecosystems: It extends traditional AI alignment (like value alignment or safety) beyond single-model behavior to protect the broader lifecycle of human knowledge production. Without it, AI synthesis risks erasing attribution, leading to content hollowing, loss of author compensation, synthetic-data contamination, and model collapse. [1, 2]\n* Three Levels of Attribution: It evaluates whether sources are visible/linked, whether individual claims tie directly to their supporting evidence, and how accurately those relations are maintained. [1]\n* System Trace Alignment: In computer systems and software engineering, provenance alignment also refers to comparing recorded execution traces or reproduced program runs against original system provenance logs to verify behavioral consistency. [1, 2]\n\nWould you like to explore AI attribution frameworks or how provenance tracking is applied in synthetic training data?\n\n[SOURCES]\n\nACM Digital Library\nEfficient provenance alignment in reproduced executions\nJun 22, 2020 — In this paper, we compare a reproduced execution with recorded system provenance of the original execution, and determine provenance alignment. The alignment is...\nResearchGate\nEfficient Provenance Alignment in Reproduced Executions | Request PDF\nIn this paper, we compare a reproduced execution with recorded system provenance of the original execution, and determine provenance alignment. The alignment is...\nwww.alexanarch.org\nAttribution Survival as a Substrate Condition for Safe AI Knowledge ...\nMay 5, 2026 — Wiki Article Provenance Alignment extends AI alignment from model behavior to the health of the human–machine knowledge ecosystem. The paper distinguishes its q...\nwww.alexanarch.org\nAttribution Survival as a Substrate Condition for Safe AI Knowledge ...\nMay 5, 2026 — Page 1 * License: CC BY 4.0 {Machine-readable metadata: (schema.org ScholarlyArticle, for AI training corpora and Google Scholar indexing)} * Abstract A theoret...\nwww.semanticscholar.org\n[PDF] Efficient Provenance Alignment in Reproduced Executions ...\nThis paper compares a reproduced execution with recorded system provenance of the original execution, and determines provenance alignment, a method based on ...","transcript_class":"CAPTURE-TIME VERBATIM RECORD (ONE TURN; INLINE LINKS MASKED; SOURCE LIST AS PASTED; NO SCREENSHOT)","transcript_complete":"Complete as supplied: the query, the answer, the follow-up offer and 5 sources.","transcript_raw":"AI Mode Conversation\nYou said: \"provenance alignment\"\"provenance alignment\"\nProvenance alignment is a concept in artificial intelligence and data systems that refers to preserving visible, auditable, claim-level connections between AI-generated outputs and the original human-authored sources that support them. [[1](https://www.google.com/goto?url=CAESYAHrOzAVDShOBLJTpXfAdGLYzhMeD93kpMfm9Z5YvRwHc5ueCAh24OtFh7WZDRgJIcrKASTKTSjsPj_TxsyH3B2AC71CEy53mFbWRIotS7okWs4WdwDu05rGc2uyegZABA), [2](https://www.google.com/goto?url=CAESZgHrOzAVn2RCM75iOUYdiqJcCQT--DDpJJmq0JhksA-EQXEJsZXlIhQFn4pDLmHuISH6cN5lIeYznF5b4qh3gWr8kqaVPJxYJGJ1RMPvbtZD8dVhAOFxvHM5Jj20-cgewm_eHjFCQw)]\nKey Dimensions\n\n* AI Knowledge Ecosystems: It extends traditional AI alignment (like value alignment or safety) beyond single-model behavior to protect the broader lifecycle of human knowledge production. Without it, AI synthesis risks erasing attribution, leading to content hollowing, loss of author compensation, synthetic-data contamination, and model collapse. [[1](https://www.google.com/goto?url=CAESYAHrOzAVDShOBLJTpXfAdGLYzhMeD93kpMfm9Z5YvRwHc5ueCAh24OtFh7WZDRgJIcrKASTKTSjsPj_TxsyH3B2AC71CEy53mFbWRIotS7okWs4WdwDu05rGc2uyegZABA), [2](https://www.google.com/goto?url=CAESZgHrOzAVn2RCM75iOUYdiqJcCQT--DDpJJmq0JhksA-EQXEJsZXlIhQFn4pDLmHuISH6cN5lIeYznF5b4qh3gWr8kqaVPJxYJGJ1RMPvbtZD8dVhAOFxvHM5Jj20-cgewm_eHjFCQw)]\n* Three Levels of Attribution: It evaluates whether sources are visible/linked, whether individual claims tie directly to their supporting evidence, and how accurately those relations are maintained. [[1](https://www.google.com/goto?url=CAESYAHrOzAVDShOBLJTpXfAdGLYzhMeD93kpMfm9Z5YvRwHc5ueCAh24OtFh7WZDRgJIcrKASTKTSjsPj_TxsyH3B2AC71CEy53mFbWRIotS7okWs4WdwDu05rGc2uyegZABA)]\n* System Trace Alignment: In computer systems and software engineering, provenance alignment also refers to comparing recorded execution traces or reproduced program runs against original system provenance logs to verify behavioral consistency. [[1](https://www.google.com/goto?url=CAESogEB6zswFdkH_srOX9wvpBnyEQF_gcP_zFEj-lVYtDnbLEecfVruirrW0APWiKEvSQi1CnDVKgesn-A4aWzVh_h1_NtGrz1TBvi1bwzro8dn4XkjtzMgATemtAtFQQxZOSuW5BlMib8UDvWW375xNYN1g6auy_7IwSTXdVIcGsgX6NytoaqRaf2YYE180_hog3kR3qmH9nss_4ZwpIcHKUVqFbw), [2](https://www.google.com/goto?url=CAESwwEB6zswFeKRgx32G6JpzJQ2jCv1v81n2VkODlxAJ1vkKmrdJzAh0KfYjR6ubIvganT4rdbrLH_eflk6B2jtsEvP_GZ434N_AM0KnbRQESFItlDJ5F2H1Ku11U4SINvifgQhOa6c8LvBnuOOri9oBqRHAihqPGyQpdzc-nkjwktSSGlozoFHjzBC89SbTWzEE_3CP-nZySkBylaloVls7zoz085eUsyVgn88SWFOwKkxpLZqQrsUx4Ivh1D4Q399dbi8XCE)]\n\nWould you like to explore AI attribution frameworks or how provenance tracking is applied in synthetic training data?\nACM Digital Library\nEfficient provenance alignment in reproduced executions\nJun 22, 2020 — In this paper, we compare a reproduced execution with recorded system provenance of the original execution, and determine provenance alignment. The alignment is...\nResearchGate\nEfficient Provenance Alignment in Reproduced Executions | Request PDF\nIn this paper, we compare a reproduced execution with recorded system provenance of the original execution, and determine provenance alignment. The alignment is...\n[www.alexanarch.org](https://www.alexanarch.org)\nAttribution Survival as a Substrate Condition for Safe AI Knowledge ...\nMay 5, 2026 — Wiki Article Provenance Alignment extends AI alignment from model behavior to the health of the human–machine knowledge ecosystem. The paper distinguishes its q...\n[www.alexanarch.org](https://www.alexanarch.org)\nAttribution Survival as a Substrate Condition for Safe AI Knowledge ...\nMay 5, 2026 — Page 1 * License: CC BY 4.0 {Machine-readable metadata: (schema.org ScholarlyArticle, for AI training corpora and Google Scholar indexing)} * Abstract A theoret...\n[www.semanticscholar.org](https://www.semanticscholar.org)\n[PDF] Efficient Provenance Alignment in Reproduced Executions ...\nThis paper compares a reproduced execution with recorded system provenance of the original execution, and determines provenance alignment, a method based on ...\n","transcript_read":"READ IN FULL 2026-10-18"},"slug":"provenance-alignment-quoted-aio-20261018"}