[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.] [QUERENT] "provenance alignment" [ANSWER] 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. [1, 2] Key Dimensions * 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] * 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] * 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] Would you like to explore AI attribution frameworks or how provenance tracking is applied in synthetic training data? [SOURCES]