Recent studies show that claims incorporating both text and images spread more effectively than those with text alone, presenting significant challenges for multimodal fact-checking. The rapid development of Multi-modal Large Language Models (MLLMs) has greatly advanced research in this field, enabling stronger performance. However, existing MLLM-based fact-checking methods fail to fully exploit visual evidence, and their reliance on rigid fine-tuning templates limits context-aware explanations and leads to weak deep reasoning. To address these limitations, we propose FACTCOMPASS, a novel framework that combines reasoning-aware fine-tuning with large-scale rule-based reinforcement learning and incorporates a semantic- and knowledge-enhanced retrieval module to strengthen deep reasoning and improve evidence utilization. This framework enhances evidence retrieval by obtaining semantically relevant evidence images, enriching the contextual understanding of claim-related images, and refining textual evidence at the knowledge level. To further enhance reasoning, we introduce a self-refining reinforcement fine-tuning strategy: (1) distilling GPT-4o's reasoning from partially fact-checking data for cold-start Chain-of-Thought learning; (2) activating reasoning across broader datasets using prior knowledge and rejection sampling; (3) applying Group Relative Policy Optimization to explore diverse reasoning paths and optimize factual consistency. Extensive experiments have demonstrated the effectiveness of the proposed framework.

Human-object interaction (HOI) detection aims to detect the spatial positions of human-object pairs and recognize their interactions. Existing single-branch, two-branch, and three-branch methods are challenging to make an appropriate trade-off on efficiency, multi-task decoupling, and collaborative learning, while they fail to identify rare and complex interaction categories effectively as well. In this work, we propose a novel Efficient Mamba-based Disentangled Progressive Learning (HOIMamba) for HOI Detection to absorb the advantages of the existing three approaches and adaptively aggregate multi-level interaction semantics guided by cross-task bidirectional information contexts. Specifically, HOIMamba builds an efficient and effective decoder through cascaded Low-Rank Adaptations (LoRAs), with high efficiency, thorough decoupling of tasks, and good multi-task collaborative learning. Furthermore, to alleviate the recognition problem of interactions in difficult HOI samples, a novel Mamba-based comprehensive progressive learning strategy with Cross-enhance Mamba (CEM) blocks and Detection Context Propagation (DCP) blocks is designed to gradually excavate interaction-related discriminative cues from four levels. CEM blocks automatically aggregate context to generate diverse task-shared semantics and simultaneously realize the cross-task interaction between human and object branches, guiding the interaction branch to extract more expressive HOI representation. DCP blocks further transfer the comprehensive interaction context to human and object branches to achieve rich and effective information exchange, facilitating the model to discover more HOI instances. Extensive experimental results on two standard benchmarks demonstrate the effectiveness of our HOIMamba.

Test-time adaptation using vision- language models (such as CLIP) to quickly adjust to distributional shifts of downstream tasks has shown great potential. Despite significant progress, existing methods are still limited to single- task test- time adaptation scenarios and have not effectively explored the issue of multi- task adaptation. To address this practical problem, we propose a novel Hierarchical Knowledge Prompt Tuning (HKPT) method, which achieves joint adaptation to multiple target domains by mining more comprehensive source domain discriminative knowledge and hierarchically modeling task- specific and task- shared knowledge. Specifically, HKPT constructs a CLIP prompt distillation framework that utilizes the broader source domain knowledge of large teacher CLIP to guide prompt tuning for lightweight student CLIP from multiple views during testing. Meanwhile, HKPT establishes task- specific dual dynamic knowledge graph to capture fine- grained contextual knowledge from continuous test data. To fully exploit the complementarity among multiple target tasks, HKPT employs an adaptive task grouping strategy for achieving intertask knowledge sharing. Furthermore, HKPT can seamlessly transfer to basic single- task test- time adaptation scenarios while maintaining robust performance. Extensive experimental results in both multi- task and single- task testtime adaptation settings demonstrate that our HKPT significantly outperforms state- of- the- art methods.

The rapid spread of multimodal misinformation on social media has raised growing concerns, while research on video misinformation detection remains limited due to the lack of large-scale, diverse datasets. Existing methods often overfit to rigid templates and lack deep reasoning over deceptive content. To address these challenges, we introduce FakeVV, a large-scale benchmark comprising over 100,000 video-text pairs with fine-grained, interpretable annotations. In addition, we further propose Fact-R1, a novel framework that integrates deep reasoning with collaborative rule-based reinforcement learning. Fact-R1 is trained through a three-stage process: (1) misinformation long-Chain-of-Thought (CoT) instruction tuning, (2) preference alignment via Direct Preference Optimization (DPO), and (3) Group Relative Policy Optimization (GRPO) using a novel verifiable reward function. This enables Fact-R1 to exhibit emergent reasoning behaviors comparable to those observed in advanced text-based reinforcement learning systems, but in the more complex multimodal misinformation setting. Our work establishes a new paradigm for misinformation detection, bridging large-scale video understanding, reasoning-guided alignment, and interpretable verification.

The dynamic nature of real-world information demands efficient knowledge editing in multimodal large language models (MLLMs) to ensure continuous knowledge updates. However, existing methods often struggle with precise matching in large-scale knowledge retrieval and lack multi-level guidance for coordinated editing, leading to less reliable outcomes. To tackle these challenges, we propose CARML, a novel retrieval-augmented editing framework that integrates conflict-aware dynamic retrieval with multi-level implicit and explicit guidance for reliable lifelong multimodal editing. Specifically, CARML introduces intra-modal uncertainty and inter-modal conflict quantification to dynamically integrate multi-channel retrieval results, so as to pinpoint the most relevant knowledge to the incoming edit samples. Afterwards, an edit scope classifier discerns whether the edit sample semantically aligns with the edit scope of the retrieved knowledge. If deemed in-scope, CARML refines the retrieved knowledge into information-rich continuous prompt prefixes, serving as the implicit knowledge guide. These prefixes not only include static knowledge prompt that capture key textual semantics but also incorporate token-level, context-aware dynamic prompt to explore fine-grained cross-modal associations between the edit sample and retrieved knowledge. To further enhance reliability, CARML incorporates a "hard correction" mechanism, leveraging explicit label knowledge to adjust the model’s output logits. Extensive experiments across multiple MLLMs and datasets indicate the superior performance of CARML in lifelong multimodal editing scenarios.

With the rapid growth of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information. However, few researchers focus on geographic natural language processing, and there has never been a benchmark to build a unified standard. In this work, we propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks, including geographic textual similarity on recall, geographic textual similarity on rerank, geographic elements tagging, geographic composition analysis, geographic where what cut, and geographic entity alignment. We also provide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark ( https://modelscope.cn/datasets/iic/GeoGLUE/summary ).

Recently, misinformation incorporating both texts and images has been disseminated more effectively than those containing text alone on social media, raising significant concerns for multi-modal fact-checking. Existing research makes contributions to multi-modal feature extraction and interaction, but fails to fully enhance the valuable semantic representations or excavate the intricate entity information. Besides, existing multi-modal fact-checking datasets are primarily focused on English and merely concentrate on a single type of misinformation, thereby neglecting a comprehensive summary and coverage of various types of misinformation. Taking these factors into account, we construct the first large-scale Chinese Multi-modal Fact-Checking (CMFC) dataset which encompasses 46,000 claims. The CMFC covers all types of misinformation for fact-checking and is divided into two sub-datasets, Collected Chinese Multi-modal Fact-Checking (CCMF) and Synthetic Chinese Multi-modal Fact-Checking (SCMF). To establish baseline performance, we propose a novel Entity-enhanced and Stance Checking Network (ESCNet), which includes Multi-modal Feature Extraction Module, Stance Transformer, and Entity-enhanced Encoder. The ESCNet jointly models stance semantic reasoning features and knowledge-enhanced entity pair features, in order to simultaneously learn effective semantic-level and knowledge-level claim representations. Our work offers the first step and establishes a benchmark for evidence-based, multi-type, multi-modal fact-checking.

Recently, falsified claims incorporating both text and images have been disseminated more effectively than those containing text alone, raising significant concerns for multi-modal fact verification. Existing research makes contributions to multi-modal feature extraction and interaction, but fails to fully utilize and enhance the valuable and intricate semantic relationships between distinct features. Moreover, most detectors merely provide a single outcome judgment and lack an inference process or explanation. Taking these factors into account, we propose a novel Explainable and Context-Enhanced Network (ECENet) for multi-modal fact verification, making the first attempt to integrate multi-clue feature extraction, multi-level feature reasoning, and justification (explanation) generation within a unified framework. Specifically, we propose an Improved Coarse- and Fine-grained Attention Network, equipped with two types of level-grained attention mechanisms, to facilitate a comprehensive understanding of contextual information. Furthermore, we propose a novel justification generation module via deep reinforcement learning that does not require additional labels. In this module, a sentence extractor agent measures the importance between the query claim and all document sentences at each time step, selecting a suitable amount of high-scoring sentences to be rewritten as the explanation of the model. Extensive experiments demonstrate the effectiveness of the proposed method.

The explosion of multimodal fake news content on social media has sparked widespread concern. Existing multimodal fake news detection methods have made significant contributions to the development of this field, but fail to adequately exploit the potential semantic information of images and ignore the noise embedded in news entities, which severely limits the performance of the models. In this paper, we propose a novel Hierarchical Semantic Enhancement Network (HSEN) for multimodal fake news detection by learning text-related image semantic and precise news high-order knowledge semantic information. Specifically, to complement the image semantic information, HSEN utilizes textual entities as the prompt subject vocabulary and applies reinforcement learning to discover the optimal prompt format for generating image captions specific to the corresponding textual entities, which contain multi-level cross-modal correlation information. Moreover, HSEN extracts visual and textual entities from image and text, and identifies additional visual entities from image captions to extend image semantic knowledge. Based on that, HSEN exploits an adaptive hard attention mechanism to automatically select strongly related news entities and remove irrelevant noise entities to obtain precise high-order knowledge semantic information, while generating attention mask for guiding cross-modal knowledge interaction. Extensive experiments show that our method outperforms state-of-the-art methods.

Query and point of interest (POI) matching is a core task in location-based services~(LBS), e.g., navigation maps. It connects users' intent with real-world geographic information. Lately, pre-trained language models (PLMs) have made notable advancements in many natural language processing (NLP) tasks. To overcome the limitation that generic PLMs lack geographic knowledge for query-POI matching, related literature attempts to employ continued pre-training based on domain-specific corpus. However, a query generally describes the geographic context (GC) about its destination and contains mentions of multiple geographic objects like nearby roads and regions of interest (ROIs). These diverse geographic objects and their correlations are pivotal to retrieving the most relevant POI. Text-based single-modal PLMs can barely make use of the important GC and are therefore limited. In this work, we propose a novel method for query-POI matching, namely Multi-modal Geographic language model (MGeo), which comprises a geographic encoder and a multi-modal interaction module. Representing GC as a new modality, MGeo is able to fully extract multi-modal correlations to perform accurate query-POI matching. Moreover, there exists no publicly available query-POI matching benchmark. Intending to facilitate further research, we build a new open-source large-scale benchmark for this topic, i.e., Geographic TExtual Similarity (GeoTES). The POIs come from an open-source geographic information system (GIS) and the queries are manually generated by annotators to prevent privacy issues. Compared with several strong baselines, the extensive experiment results and detailed ablation analyses demonstrate that our proposed multi-modal geographic pre-training method can significantly improve the query-POI matching capability of PLMs with or without users' locations. Our code and benchmark are publicly available at https://github.com/PhantomGrapes/MGeo.