Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs

• URL: http://arxiv.org/abs/2509.05714v1
• Date: Sat, 06 Sep 2025 13:26:04 GMT
• Title: Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
• Authors: Zhaoyu Fan, Kaihang Pan, Mingze Zhou, Bosheng Qin, Juncheng Li, Shengyu Zhang, Wenqiao Zhang, Siliang Tang, Fei Wu, Yueting Zhuang,
• Abstract summary: We introduce CogEdit, a novel benchmark designed to evaluate MLLMs' meta-cognitive knowledge editing abilities.<n>We propose MIND, 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.
• Score: 71.8547241246169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information.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.

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