ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
arXiv preprint arXiv:2412.12140 , year =
2 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
AI agents lack the persistent identity and feedback mechanisms needed for consequence reception, requiring new architectures or continued human accountability.
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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Some[Body] Must Receive That Pain for Agent Accountability
AI agents lack the persistent identity and feedback mechanisms needed for consequence reception, requiring new architectures or continued human accountability.