AI 速览：作者提出首个面向多模态有状态交互的大规模多轮对话数据集 YANAMI-400K 与评测基准 YANAMI-Bench，并报告微调 BAGEL 后在图像编辑、生成与有状态交互上均有提升。
Unified Multimodal Models (UMMs) integrate visual understanding, generation, and editing within a single unified framework. However, existing unified multimodal datasets and benchmarks remain dominated by single-turn tasks. Even in recent multi-turn datasets, many turns can be solved independently without tracking information across turns. Consequently, they provide limited supervision and evaluation for multimodal stateful interaction, which requires models to understand, maintain, update, and faithfully realize evolving dialogue states across turns. To address this gap, we introduce YANAMI, the first large-scale multimodal dialogue dataset and benchmark for stateful interaction, comprising YANAMI-400K and YANAMI-Bench. YANAMI-400K contains 400K multi-turn multimodal dialogues, with more than 2M images and 2.6M dialogue turns. The dialogues span four representative scenarios: Multi-Image Fusion, Image Editing, World Modeling, and Visual Reasoning, and require models to continuously track, preserve, and revise evolving multimodal dialogue states across turns. YANAMI-Bench comprises 400 multi-turn dialogues for systematically evaluating multimodal stateful interaction. It jointly evaluates Dialogue State Understanding and Visual Realization, characterizing the gap between models' ability to understand evolving dialogue states and their ability to faithfully realize them in visual outputs. Experiments show that fine-tuning BAGEL on YANAMI-400K improves image editing, generation, and multimodal stateful interaction by 10.3%, 2.0%, and 13.23%, respectively, while preserving its general visual understanding capabilities. Evaluations on YANAMI-Bench further reveal that existing UMMs still struggle to maintain, update, and visually realize evolving multimodal dialogue states.
Zhuyi Zhao, Shengjie Yuan, Shengyu Wang, Kang Wei, Wang Yang, Wenchao Xu, Fushuo Huo
Single-pass scoring models such as Jev give each code function a vulnerability probability, so that security teams can rank many functions for review without generating written explanations. Training an open-weight model for this task is limited by data. Benchmarks with both vulnerable and non-vulnerable labels keep few vulnerable functions once examples related to the test data are removed, while larger public vulnerability records mostly contain vulnerable functions only. Mixing the two lets the data source predict the label, and training on the labeled data alone leaves a model that often scores a fixed function above its vulnerable original. We present Cyber-JEV, a fine-tuning method that uses fix commits to turn these records into comparison groups. Each group holds a vulnerable function, its patched version and up to two unchanged functions from the same file, so the model sees the change that removed a recorded defect. One shared model learns two tasks: classifying functions from the labeled reference data, and ranking the vulnerable function above the other members of its group. At test time the input is one function and the output is a vulnerability score. On a test benchmark of 7,270 functions, an ensemble of five fine-tuned Qwen3.5-4B models reaches an area under the receiver operating characteristic curve of 0.814, compared with 0.743 for Jev 1.13. With the 4,031 alerts of Jev as the review budget, it misses 16 of 322 vulnerable functions instead of 39. Compared with training on the reference data alone, it ranks the vulnerable version above the patched version 14.8 to 29.2 percentage points more often on three sets of real fixes. Such scores can order the changed functions of a pull request or a release for review under a fixed budget.
Linghui Jiao, Kang Wei, Fushuo Huo, Ruijie Zhao, Yiyang Ni, Jingcai Guo, Yipeng Zhou, Jun Li
Retrieval-Augmented Generation (RAG) enhances the factual grounding of Large Language Models (LLMs) by incorporating external evidence, but also exposes them to corpus poisoning attacks. In particular, coordinated attacks can introduce multiple mutually reinforcing poisoned documents into retrieved contexts, making reliable evidence filtering challenging. To address this problem, we propose GuardRAG, a training-free and plug-and-play post-retrieval defense that exploits the semantic structure of retrieved evidence to identify suspicious document groups. Specifically, GuardRAG first expands the retrieval range and removes near-duplicate candidates, then partitions the retained documents using Hierarchical Agglomerative Clustering (HAC) with a query-adaptive threshold. Finally, it scores and prunes suspicious clusters based on query relevance, cluster size, and semantic compactness. Experiments across three question-answering datasets, multiple poisoning attacks, and six LLMs demonstrate that GuardRAG substantially reduces attack success rates while maintaining answer utility under clean retrieval.
Cui Yakun The Hong Kong University of Science and Technology, Peng Qi Centre for Trusted Internet and Community, National University of Singapore, Fushuo Huo The Hong Kong University of Science and Technology, Hang Du Beijing University of Posts and Telecommunications, Weijie Shi Hong Kong University of Science and Technology, Juntao Dai Peking University, Zhenghao Zhu, Sirui Han The Hong Kong University of Science and Technology
The advent of multi-modal large language models (MLLMs) has greatly advanced research on video fake news detection (VFND) tasks. Existing benchmarks typically focus on the detection accuracy, while failing to provide fine-grained assessments for the entire detection process. To address these limitations, we introduce POVFNDB (Process-oriented Video Fake News Detection Benchmark), a process-oriented benchmark comprising 10 tasks designed to systematically evaluate MLLMs’ perception, understanding, and reasoning capabilities in VFND. This benchmark contains 36,240 human-annotated question-answer (QA) in structured or open-ended formats, spanning 15 distinct evaluation dimensions that characterize different aspects of the video fake news detection process.Using POVFNDB, we conduct comprehensive evaluations on both proprietary and open-source MLLMs. Moreover, We fine-tune Qwen2.5VL-7B-Instruct on a reasoning dataset generated by our proposed POVFND-CoT, a chain-of-thought method that utilizes rationales from evaluation results and rationale validation. The resulting model achieves sota performance on VFND.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表