Even when factually correct, social-media news previews (image-headline pairs) can induce interpretation drift: by selectively omitting crucial context, they lead readers to form judgments that diverge from what the full article supports. This covert harm is subtler than explicit misinformation, yet remains underexplored. To address this gap, we develop a multi-stage pipeline that simulates preview-based and context-based understanding, enabling construction of the MM-Misleading benchmark. Using MM-Misleading, we systematically evaluate open-source LVLMs and uncover pronounced blind spots in omission-based misleadingness detection. We further propose OMGuard, which combines (1) Interpretation-Aware Fine-Tuning for misleadingness detection and (2) Rationale-Guided Misleading Content Correction, where explicit rationales guide headline rewriting to reduce misleading impressions. Experiments show that OMGuard lifts an 8B model’s detection accuracy to the level of a 235B LVLM while delivering markedly stronger end-to-end correction. Further analysis shows that misleadingness usually arises from local narrative shifts, such as missing background, instead of global frame changes, and identifies image-driven cases where text-only correction fails, underscoring the need for visual interventions.

Although Knowledge Editing provides an efficient mechanism for updating the knowledge of Multimodal Large Language Models (MLLMs), we find that current paradigms still suffer from an important yet remain underexplored issue : editing decoupling failure, where entity-related knowledge can be updated when the model is triggered by multimodal inputs (text–image query pairs), however, it often reverts to outdated pre-edit facts when the paired inputs are split into unimodal ones. Our in-depth empirical analysis reveals that the entity knowledge in MLLMs is not stored as a unified representation, but is instead distributed across disentangled modality-specific pathways. As a result, updates biased toward multimodal queries fail to propagate effectively to unimodal circuits. To bridge this gap, we propose DECODE, which explicitly disentangles and localizes modality-specific neuron groups for targeted knowledge. Extensive experiments demonstrate that DECODE consistently achieves effective knowledge updates under different modality triggers, thereby mitigating editing decoupling failures.

The widespread use of social networks has significantly accelerated the dissemination of information but has also facilitated the rapid spread of fake news, leading to various negative consequences. Recently, with the emergence of large language models (LLMs), researchers have focused on leveraging LLMs for automated fake news detection. Unfortunately, many issues remain to be addressed. First, the evidence retrieved to verify given fake news is often insufficient, limiting the performance of LLMs when reasoning directly from this evidence. Additionally, the retrieved evidence frequently contains substantial redundant information, which can interfere with the LLMs’ judgment. To address these limitations, we propose a Multiple Knowledge Sources Retrieval and LLM Knowledge Conversion framework, which enriches the evidence available for claim verification. We also introduce a Redundant Information Filtering Strategy, which minimizes the influence of irrelevant information on the LLM reasoning process. Extensive experiments conducted on two challenging fact-checking datasets demonstrate that our proposed method outperforms state-of-the-art fact-checking baselines. Our code is available at https://github.com/quark233/IMRRF/tree/main.