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 "title": "Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition",
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 "date": "2026-05-05",
 "description": "A theoretical AI-alignment paper defining **provenance alignment** as the structural preservation of visible, auditable, claim-level relations between AI-composed outputs and the human-authored sources on which they depend. It argues that attribution is not merely citation hygiene but a condition for maintaining the knowledge substrate that future AI systems require.\n\nThe paper proposes—but does not claim to have proved—a pathway from provenance erasure to weakened author return, content hollowing, synthetic-data contamination, increased model-collapse risk, and semantic exhaustion. Provenance Erasure Rate measures missing attribution at source and claim level; a proposed Provenance Failure Rate would address citations that remain present while the underlying ontological relation is wrong.",
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 "wiki_article": "*Provenance Alignment* extends AI alignment from model behavior to the health of the human–machine knowledge ecosystem.\n\nThe paper distinguishes its question from four established alignment questions:\n\n- value alignment;\n- Constitutional AI;\n- catastrophic-risk and safety alignment;\n- scalable oversight.\n\nThose fields generally assume continued access to high-quality human judgment and content. Provenance alignment asks whether AI composition preserves the conditions under which that material will continue to be produced.\n\nIts formal definition is:\n\n> Provenance alignment is the structural property of an AI knowledge-composition system whereby source-dependent claims preserve visible, auditable, claim-level relations to the human-authored sources that made the claims possible.\n\nThe paper separates three attribution levels:\n\n1. **Source visibility** — is the source linked?\n2. **Claim-source attribution** — is each dependent claim tied to its support?\n3. **Relational provenance** — is the source’s actual ontological relation preserved?\n\nPER measures the first two. The proposed PFR addresses the third.\n\nThree claim types require different handling:\n\n- **Retrieval-dependent claims** are tied to documents present at composition time.\n- **Synthesis claims** require distributed or multi-source attribution.\n- **Parametric claims** arise from model weights and require training-data provenance infrastructure beyond ordinary runtime citation.\n\nThe proposed degradation pathway is:\n\n1. AI systems erase provenance.\n2. Citation, traffic, reputation, and licensing return to authors weakens.\n3. Open human production declines or retreats behind barriers.\n4. Synthetic substitutes occupy more of the public corpus.\n5. Later models train on increasingly degraded material.\n6. Model-collapse risk rises, approaching semantic exhaustion in computational form.\n\nThe paper explicitly states that provenance erasure is neither the sole cause of content hollowing nor a proved sole cause of model collapse. The individual author-disincentive link requires empirical study; synthetic-data collapse is supported by separate research; PER supplies a proposed instrument for testing the attribution channel.\n\nProvenance alignment is proposed as a candidate principle for AI search, retrieval-augmented generation, Constitutional AI, and attribution governance. The design goal is to keep PER near zero and preserve the public feedback loop between use and credit.",
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