The advancement of multimodal large language models (MLLMs) has enabled impressive perception capabilities. However, their reasoning process often remains a "fast thinking" paradigm, reliant on end-to-end generation or explicit, language-centric chains of thought (CoT), which can be inefficient, verbose, and prone to hallucination. This work posits that robust reasoning should evolve within a latent space, integrating multimodal signals seamlessly. We propose multimodal latent reasoning via HIerarchical Visual cuEs injection (\emph{HIVE}), a novel framework that instills deliberate, "slow thinking" without depending on superficial textual rationales. Our method recursively extends transformer blocks, creating an internal loop for iterative reasoning refinement. Crucially, it injectively grounds this process with hierarchical visual cues from global scene context to fine-grained regional details directly into the model's latent representations. This enables the model to perform grounded, multi-step inference entirely in the aligned latent space. Extensive evaluations demonstrate that test-time scaling is effective when incorporating vision knowledge, and that integrating hierarchical information significantly enhances the model's understanding of complex scenes.
Action Anticipation at a Glimpse: To What Extent Can Multimodal Cues Replace Video?
Universidad de Alicante（阿利坎特大学）；Foundation for Research and Technology-Hellas（希腊基础研究与技术基金会）；University of Crete（克里特大学）；Hellenic Mediterranean University（希腊地中海大学）
StructXLIP: Enhancing Vision-language Models with Multimodal Structural Cues
University of Verona（维罗纳大学）；Sapienza University of Rome（罗马大学）；University of Trento（特伦托大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Reykjavik University（雷克雅未克大学）
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（基础模型研究中心，自动化研究所，中国科学院）；ByteDance（字节跳动）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究所）；Xi’an Jiaotong University（西安交通大学）；Renmin University of China（中国人民大学）
Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models
National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；University of Science and Technology of China（中国科学技术大学）；University of Electronic Science and Technology of China（电子科技大学）；University of California, Berkeley（加州大学伯克利分校）
StylisticBias: A Few Human Visual Cues Drive Most Social Biases in MLLMs
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Princeton Center for Information and Technology Policy（普林斯顿信息与技术政策中心）
CLARA: Clip-Level Multimodal Alignment with VLM-Derived Rationales for Hateful Video Detection
Institute for Analytics and Data Science, University of Essex（埃塞克斯大学分析与数据科学研究所）；University of Exeter（埃克塞特大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）
MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance
KT Corporation, Republic of Korea（韩国KT公司）；Graduate School of Artificial Intelligence, POSTECH, Republic of Korea（POSTECH人工智能研究生院）；Computer Science and Engineering, POSTECH, Republic of Korea（POSTECH计算机科学与工程系）
In this paper, we presented a preliminary study for tactical driver behavior detection from untrimmed naturalistic driving recordings. While supervised learning based detection is a common approach, it suffers when labeled data is scarce. Manual annotation is both time-consuming and expensive. To emphasize this problem, we experimented on a 104-hour real-world naturalistic driving dataset with a set of predefined driving behaviors annotated. There are three challenges in the dataset. First, predefined driving behaviors are sparse in a naturalistic driving setting. Second, the distribution of d…
Reasoning-OCR: Can Large Multimodal Models Solve Complex Logical Reasoning Problems from OCR Cues?
School of Computer Science, National Engineering Research Center for Multimedia Software, and Institute of Artificial Intelligence, Wuhan University, China（计算机学院、多媒体软件国家工程研究中心及人工智能研究所、武汉大学）；College of Computing & Data Science at Nanyang Technological University（南洋理工大学计算与数据科学学院）
CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization
School of Computer Science and Engineering（计算机科学与工程学院）；MoE Key Laboratory of Information Technology（信息技术关键实验室）；Key Laboratory of Information Security Technology（信息安全技术重点实验室）；Sun Yat-sen University（中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Alibaba Group（阿里巴巴集团）
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹信息与健康数据科学学院（HIDSS4Health））；Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所（IKIM））
Video caption refers to generating a descriptive sentence for a specific short video clip automatically, which has achieved remarkable success recently. However, most of the existing methods focus more on visual information while ignoring the synchronized audio cues. We propose three multimodal deep fusion strategies to maximize the benefits of visual-audio resonance information. The first one explores the impact on cross-modalities feature fusion from low to high order. The second establishes the visual-audio short-term dependency by sharing weights of corresponding front-end networks. The th…
Manipulating Multimodal Agents via Cross-Modal Prompt Injection
Beihang University（北洋大学）；National University of Singapore（新加坡国立大学）；Huazhong University of Science and Technology（华中科技大学）；Henan University of Science and Technology（河南科技大学）
MLCR: Multi-Level Cue Refinement for Long-Term Multimodal Action Quality Assessment
Qiushi Academy for Advanced Studies (QAAS), Zhejiang University（浙江大学启斯特先进研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；State Key Lab of Brain-Machine Intelligence（脑机智能国家重点实验室）；Collaborative Innovation Center for Artificial Intelligence by MOE and Zhejiang Provincial Government (ZJU)（教育部-浙江省人工智能协同创新中心）；Zhejiang Lab（浙江实验室）
University of Tübingen（图宾根大学）；University of Trento（特伦托大学）；Hector Research Institute of Education Sciences and Psychology（赫克托教育科学与心理学研究所）
Public speaking and presentation competence plays an essential role in many areas of social interaction in our educational, professional, and everyday life. Since our intention during a speech can differ from what is actually understood by the audience, the ability to appropriately convey our message requires a complex set of skills. Presentation competence is cultivated in the early school years and continuously developed over time. One approach that can promote efficient development of presentation competence is the automated analysis of human behavior during a speech based on visual and aud…
Indian Institute of Technology Ropar（印度理工学院罗巴尔分校）；University of Canberra（堪培拉大学）
While personality traits have been extensively modeled as behavioral constructs, we model \textbf{\textit{job hirability}} as a \emph{personality construct}. On the {\emph{First Impressions Candidate Screening}} (FICS) dataset, we examine relationships among personality and hirability measures. Modeling hirability as a discrete/continuous variable with the \emph{big-five} personality traits as predictors, we utilize (a) apparent personality annotations, and (b) personality estimates obtained via audio, visual and textual cues for hirability prediction (HP). We also examine the efficacy of a tw…
Jacobs University Bremen（不来梅雅各布大学）；ShanghaiTech University（上海科技大学）
Underwater robot interventions require a high level of safety and reliability. A major challenge to address is a robust and accurate acquisition of localization estimates, as it is a prerequisite to enable more complex tasks, e.g. floating manipulation and mapping. State-of-the-art navigation in commercial operations, such as oil & gas production (OGP), rely on costly instrumentation. These can be partially replaced or assisted by visual navigation methods, especially in deep-sea scenarios where equipment deployment has high costs and risks. Our work presents a multimodal approach that adapts …
ArtCognition: A Multimodal AI Framework for Affective State Sensing from Visual and Kinematic Drawing Cues
Department of Electrical and Computer Engineering, University of Tehran（电信工程系，德黑兰理工大学）；Tehran Institute for Advanced Studies, Khatam University（德黑兰高级研究院，凯塔姆大学）；Department of Psychology, Allameh Tabataba’i University（心理学系，阿勒梅·塔巴塔比大学）
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；Adobe Research（Adobe 研究院）
Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced on-line iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural netwo…
Beijing University of Posts and Telecommunications（北京邮电大学）
Predicting the plausible future trajectories of nearby agents is a core challenge for the safety of Autonomous Vehicles and it mainly depends on two external cues: the dynamic neighbor agents and static scene context. Recent approaches have made great progress in characterizing the two cues separately. However, they ignore the correlation between the two cues and most of them are difficult to achieve map-adaptive prediction. In this paper, we use lane as scene data and propose a staged network that Jointly learning Agent and Lane information for Multimodal Trajectory Prediction (JAL-MTP). JAL-…
XEmoGPT: An Explainable Multimodal Emotion Recognition Framework with Cue-Level Perception and Reasoning
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；th Research Institute, China Electronics Technology Group Corporation（中国电子科技集团第五十四研究所）
