{"ID":2873969,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05714","arxiv_id":"2509.05714","title":"Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs","abstract":"Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information. However, existing benchmarks primarily emphasize cognitive-level modifications while lacking a focus on deeper meta-cognitive processes. To bridge this gap, we introduce CogEdit, a novel benchmark designed to evaluate MLLMs' meta-cognitive knowledge editing abilities across three levels: (1) Counterfactual-Driven Editing, assessing self-awareness of knowledge correctness changes; (2) Boundary Constraint Editing, ensuring appropriate generalization without unintended interference; and (3) Noise-Robust Editing, promoting reflective evaluation of uncertain information. To advance meta-cognitive editing, we propose MIND (Meta-cognitive INtegrated Dynamic Knowledge Editing), a framework that constructs a meta-knowledge memory for self-awareness, employs game-theoretic interactions to monitor knowledge activation, and incorporates label refinement for noise-robust updates. Extensive experiments show that MIND significantly outperforms existing cognitive editing approaches, achieving strong performance on both traditional and meta-cognitive knowledge editing benchmarks.","short_abstract":"Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information. However, existing benchmarks primarily emphasize cognitive-level modifications while lacking a focus on deeper meta-cognitive processes. To bridge this gap, we introduce CogEdit, a novel benchmark...","url_abs":"https://arxiv.org/abs/2509.05714","url_pdf":"https://arxiv.org/pdf/2509.05714v1","authors":"[\"Zhaoyu Fan\",\"Kaihang Pan\",\"Mingze Zhou\",\"Bosheng Qin\",\"Juncheng Li\",\"Shengyu Zhang\",\"Wenqiao Zhang\",\"Siliang Tang\",\"Fei Wu\",\"Yueting Zhuang\"]","published":"2025-09-06T13:26:04Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CV\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}