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The article explores designing memory systems for AI agents that are auditable by humans, suggesting fields like provenance, scope, and expiration rules to maintain clarity and prevent stale information.
This paper introduces a provenance-guided incremental learning framework to handle rule-induced concept shifts, where target definitions change, and evaluates it on a new benchmark with improved efficiency and accuracy.
OriginBlame is a record- and token-level data provenance system that propagates author identity through AI training data pipelines, enabling precise forget sets for machine unlearning. It eliminates over-deletion from dataset-level systems and improves unlearning effectiveness.
This paper introduces a novel dataset watermarking method for closed LLMs that uses co-occurrence patterns of word pairs to provably detect if proprietary data was used in training, even when it constitutes a small fraction of the dataset.