Multimodal Knowledge Editing (MKE) extends traditional knowledge editing to settings involving both textual and visual modalities. However, existing MKE benchmarks primarily assess final answer correctness, neglecting the quality of intermediate reasoning and robustness to visually rephrased inputs. To address this limitation, we introduce MMQAKE, the first benchmark for multimodal multihop question answering with knowledge editing. MMQAKE evaluates: (1) a model’s ability to reason over 2–5-hop factual chains that span both text and images, including performance at each intermediate step; (2) robustness to visually rephrased inputs in multihop questions. Our evaluation shows that current MKE methods often struggle to consistently update and reason over multimodal reasoning chains following knowledge edits. To overcome these challenges, we propose Hybrid-DMKG, a hybrid reasoning framework built on a dynamic multimodal knowledge graph (DMKG) to enable accurate multihop reasoning over updated multimodal knowledge. Hybrid-DMKG first uses a large language model to decompose multimodal multihop questions into sequential sub-questions, then applies a multimodal retrieval model to locate updated facts by jointly encoding each sub-question with candidate entities and their associated images. For answer inference, a hybrid reasoning module operates over the DMKG via two parallel paths: (1) relation-linking prediction; (2) RAG Reasoning with large vision-language models. A background-reflective decision module then aggregates evidence from both paths to select the most credible answer. Experimental results on MMQAKE show that Hybrid-DMKG significantly outperforms existing MKE approaches, achieving higher accuracy and improved robustness to knowledge updates.

In recent years, multimodal topic models have gained significant attention in various tasks involving short texts. Despite their impressive results, most models rely on bag-of-words assumptions for each modality, neglecting the intrinsic word relations in the textual modality and the underlying object relations in the visual modality. To address this limitation, we propose a novel approach that represents each document modality as a graph, harnessing the word relations and the visual object relations to guide the topic extraction process. Our approach is grounded in the insight that, in the textual modality, words with specific relations, such as co-occurrence relations, semantic relations and syntactic relations, are more likely to be assigned to the same topic. Similarly, in the visual modality, the relations between objects, such as spatial relations and contextual relations, can also provide valuable information for topic extraction. By leveraging graph-based representations, our model captures the inherent associations between words and visual objects, resulting in the generation of more coherent and interpretable topics. To infer the model's parameters, we develop an effective algorithm that integrates neural variational inference and contrastive learning. The experimental results on three datasets verify the effectiveness of our proposed model in terms of topic coherence, topic diversity and mean average precision, confirming that incorporating word relations and object relations through graph-based representations significantly enhances the quality of the extracted topics.