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Hypergraph-based failure attribution can boost LLM reasoning accuracy by efficiently pinpointing the root causes of errors, outperforming traditional methods.
Existing Multi-modality Machine Unlearning methods are fundamentally flawed, allowing adversaries to recover nearly all supposedly erased sensitive information from MLLMs.
