Agents & InferencearXiv
OriginBlame cuts over-deletion from 101x to 1.3x with record-level data provenance
Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated.
Summary A
Data provenance systems can now pinpoint exact training records to be removed for a given author, reducing dataset-level over-deletion from 101x to 1.3x, and this capability enables model trainers to implement unlearning algorithms 42% more effectively, directly impacting the efficiency of handling data removal requests in production LLM environments.
Summary B
Record-level provenance cuts over-deletion from 101× to 1.3× when honoring data-removal requests. This means you can now comply with revocation demands without nuking entire datasets or retraining from scratch, saving weeks of compute and preserving model performance—just add 1–4% pipeline overhead.
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