Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs

2025-09-06

AI Summary

Existing knowledge editing benchmarks for multimodal large language models (MLLMs) focus exclusively on cognitive-level modifications, neglecting meta-cognitive capabilities—such as self-awareness, boundary adherence, and noise robustness. Method: We propose the first meta-cognitive knowledge editing evaluation framework, comprising three dimensions: counterfactual-driven editing, boundary-constrained editing, and noise-robust editing. To operationalize this, we introduce MIND—a novel framework integrating meta-knowledge memory modeling, game-theoretic knowledge activation monitoring, and dynamic label refinement, enabling self-monitoring and reflective reasoning during editing. Contribution/Results: Extensive experiments demonstrate that MIND significantly outperforms state-of-the-art methods on both conventional knowledge editing benchmarks and our new meta-cognitive benchmarks, achieving substantial improvements in editing accuracy, generalization across unseen concepts, and robustness under epistemic uncertainty and input perturbations.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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Knowledge editing enables multimodal large language models (MLLMs) to efficiently update outdated or incorrect information.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.

Problem

• Evaluating meta-cognitive knowledge editing abilities in multimodal LLMs
• Assessing self-awareness of knowledge correctness changes after editing
• Ensuring appropriate generalization without unintended interference effects

Innovation

• Meta-knowledge memory for self-awareness in editing
• Game-theoretic interactions monitor knowledge activation
• Label refinement enables noise-robust knowledge updates