Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
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arXiv
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| Format: | Preprint |
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2025
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| author | Fan, Zhaoyu Pan, Kaihang Zhou, Mingze Qin, Bosheng Li, Juncheng Zhang, Shengyu Zhang, Wenqiao Tang, Siliang Wu, Fei Zhuang, Yueting |
| author_facet | Fan, Zhaoyu Pan, Kaihang Zhou, Mingze Qin, Bosheng Li, Juncheng Zhang, Shengyu Zhang, Wenqiao Tang, Siliang Wu, Fei Zhuang, Yueting |
| contents | Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information.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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05714 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs Fan, Zhaoyu Pan, Kaihang Zhou, Mingze Qin, Bosheng Li, Juncheng Zhang, Shengyu Zhang, Wenqiao Tang, Siliang Wu, Fei Zhuang, Yueting Artificial Intelligence Computer Vision and Pattern Recognition Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information.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. |
| title | Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.05714 |