Introduces Latent Adversarial Robustification and Rank-Constrained Subspace Learning to enable robust generalization in multimodal knowledge editing through adversarial subspace alignment.
Mmke-bench: A multimodal editing benchmark for diverse visual knowledge
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abstract
Knowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and multimodal models (LMMs), allowing them to correct outdated or inaccurate information without retraining from scratch. However, existing benchmarks for multimodal knowledge editing primarily focus on entity-level knowledge represented as simple triplets, which fail to capture the complexity of real-world multimodal information. To address this issue, we introduce MMKE-Bench, a comprehensive MultiModal Knowledge Editing Benchmark, designed to evaluate the ability of LMMs to edit diverse visual knowledge in real-world scenarios. MMKE-Bench addresses these limitations by incorporating three types of editing tasks: visual entity editing, visual semantic editing, and user-specific editing. Besides, MMKE-Bench uses free-form natural language to represent and edit knowledge, offering a more flexible and effective format. The benchmark consists of 2,940 pieces of knowledge and 8,363 images across 33 broad categories, with evaluation questions automatically generated and human-verified. We assess five state-of-the-art knowledge editing methods on three prominent LMMs, revealing that no method excels across all criteria, and that visual and user-specific edits are particularly challenging. MMKE-Bench sets a new standard for evaluating the robustness of multimodal knowledge editing techniques, driving progress in this rapidly evolving field.
years
2026 4representative citing papers
SMMBench is a benchmark evaluating multimodal agents on cross-source reasoning, conflict resolution, preference reasoning, and action prediction, showing current systems struggle with evidence distributed across heterogeneous sources.
M3Bench is a clinically grounded benchmark showing that gradient-based VLM editors generalize but break locality, while memory-based editors preserve locality but fail on composition and temporal tasks, with failures traced to anisotropic latent representations.
DECODE identifies and separately edits modality-specific neurons in MLLMs to prevent knowledge edits from reverting under unimodal queries.
citing papers explorer
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Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment
Introduces Latent Adversarial Robustification and Rank-Constrained Subspace Learning to enable robust generalization in multimodal knowledge editing through adversarial subspace alignment.
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SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory
SMMBench is a benchmark evaluating multimodal agents on cross-source reasoning, conflict resolution, preference reasoning, and action prediction, showing current systems struggle with evidence distributed across heterogeneous sources.
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Evaluating and Understanding Model Editing for Medical Vision Language Models
M3Bench is a clinically grounded benchmark showing that gradient-based VLM editors generalize but break locality, while memory-based editors preserve locality but fail on composition and temporal tasks, with failures traced to anisotropic latent representations.
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Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
DECODE identifies and separately edits modality-specific neurons in MLLMs to prevent knowledge edits from reverting under unimodal queries.