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MIRA: A Multimodal Illustrated Deep Research Agent for Controllable Reports
Hongrui Wu, Kaiwen Tuo, Congcong Wang +7
Deep research agents are increasingly capable of searching the open web and synthesizing evidence, yet they remain poorly suited to controllable report generation in multimodal dee…
Evidence-RL: Towards Evidence-intensive Visual Reasoning
Haojie Huang, Xinlei Yu, Chengming Xu +6
Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context. Existing perception-aware pos…
Beacon: Knowing When and How to Perform Agentic Visual Reasoning
Qixun Wang, Yang Shi, Letian Cheng +11
The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks. We rethink agentic visual reasoning th…
RefCaptioner: Multi-Reference Image-Grounded Video Captioning
Tengfei Liu, Yang Shi, Yuran Wang +16
Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-…
KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation
Yuqi Tang, Tengfei Liu, Yizheng Lai +18
Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-ke…
4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +9
Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vision-language models (VLMs). Prio…
Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models
Xiaomin Yu, Yi Xin, Yuhui Zhang +12
Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of d…
VisMem: Latent Vision Memory Unlocks Potential of Vision-Language Models
Xinlei Yu, Chengming Xu, Guibin Zhang +7
Despite the remarkable success of Vision-Language Models (VLMs), their performance on a range of complex visual tasks is often hindered by a "visual processing bottleneck": a prope…
Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Views
Zhangquan Chen, Manyuan Zhang, Xinlei Yu +8
Though recent advances in vision-language models (VLMs) have achieved remarkable progress across a wide range of multimodal tasks, understanding 3D spatial relationships from limit…
DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing
Zihan Zhou, Shilin Lu, Shuli Leng +4
Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient to project o…
SIFThinker: Spatially-Aware Image Focus for Visual Reasoning
Zhangquan Chen, Ruihui Zhao, Chuwei Luo +4
Current multimodal large language models (MLLMs) still face significant challenges in complex visual tasks (e.g., spatial understanding, fine-grained perception). Prior methods hav…
Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling
Xinlei Yu, Chengming Xu, Zhangquan Chen +6
The dominant paradigm of monolithic scaling in Vision-Language Models (VLMs) is failing for understanding and reasoning in documents, yielding diminishing returns as it struggles w…
Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation
Junrong Yue, Yifan Zhang, Chuan Qin +5
Vision-and-Language Navigation (VLN) aims to enable embodied agents to follow natural language instructions and reach target locations in real-world environments. While prior metho…