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Machine Unlearning for Large Language Models: Foundations, Advances, and Agentic Extensions
Machine unlearning aims to remove target influence while preserving other capabilities. This survey compares methods, benchmarks, and evidence across large language models and systems using retrieval, memory, tools, and interacting agents. A five-layer framework connects removal requests, system bou
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