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Machine unlearning for large language models seeks to remove specified knowledge while preserving general capability, yet current methods often treat an entire response as the unit of forgetting. This coarse granularity causes unnecessary utility loss when the forget target is only a small set of sensitive facts embedd...
GRAPHSU is proposed, a graph-guided controller that expands the deletion scope beyond forget seeds by constructing a weighted support-route graph, propagating deletion pressure through it, and applying graded forgetting strengths to high-risk neighbors, demonstrating that effective enterprise unlearning requires contro...
SWAP-Lite is proposed, a Pareto-guided evolutionary algorithm that maintains an explicit archive of non-dominated solutions over representational capacity and deployment cost, yielding an anytime optimiser that exposes budget-feasible solutions throughout the search.
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