activity
20242026
collaborators

40 papers

cs.SD2026

The Affective Bridge: Preserving Speech Representations while Enhancing Deepfake Detection vian emotional Constraints

Yupei Li, Chenyang Lyu, Longyue Wang +4

Speech deepfake detection (DFD) has benefited from diverse acoustic and semantic speech representations, many of which encode valuable speech information and are costly to train. P…

cs.CL2026

Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding

Sensen Gao, Shanshan Zhao, Xu Jiang +7

Document understanding is critical for applications from financial analysis to scientific discovery. Current approaches, whether OCR-based pipelines feeding Large Language Models (…

cs.CV2026

Walk the Talk: Bridging the Reasoning-Action Gap for Thinking with Images via Multimodal Agentic Policy Optimization

Wenhao Yang, Yu Xia, Jinlong Huang +10

Recent advancements in Multimodal Large Language Models (MLLMs) have incentivized models to ``think with images'' by actively invoking visual tools during multi-turn reasoning. The…

cs.AI2026

Building Autonomous GUI Navigation via Agentic-Q Estimation and Step-Wise Policy Optimization

Yibo Wang, Guangda Huzhang, Yuwei Hu +7

Recent advances in Multimodal Large Language Models (MLLMs) have substantially driven the progress of autonomous agents for Graphical User Interface (GUI). Nevertheless, in real-wo…

cs.AI2026

A State-Transition Framework for Efficient LLM Reasoning

Liang Zhang, Yu Zhao, Longyue Wang +4

While Long Chain-of-Thought (CoT) reasoning significantly improves Large Language Models (LLMs) performance on complex reasoning tasks, the substantial computational and memory cos…

cs.CV2026

Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images

JiaKui Hu, Shanshan Zhao, Qing-Guo Chen +6

This paper presents Omni-View, which extends the unified multimodal understanding and generation to 3D scenes based on multiview images, exploring the principle that "generation fa…