32 citations · 40 across the 24 of their papers we have counts for
14 papers · 1 filter
EgoTools: Towards Tool-Centric Reasoning in Real-World Egocentric Videos
Shulin Tian, Junsu Kim, Shuai Liu +17
Real-world embodied tasks, from everyday activities to professional procedures, require agents to act under physical constraints while tracking evolving object and task states. Too…
Video-HopChain: Multi-Hop Questions and Confidence-Gated Exploration for Video Reasoning Models
Trung Nguyen Quang, Yuhao Dong, Shuo Sun +4
HopChain has shown on still images that multi-hop data synthesis improves vision-language reasoning, because long chain-of-thought reasoning exposes errors that compound across ste…
V-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning
Shulin Tian, Minglun Li, Yuhao Dong +6
Vision-language models can produce fluent answers that are insufficiently grounded in the visual evidence: a single unsupported object, chart value, or intermediate inference can u…
VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
Xinhao Li, Yuhan Zhu, Xiangyu Zeng +24
Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current…
PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models
Zichao Lin, Yifeng Xie, Bowen Qu +30
We introduce PerceptionBench, a benchmark specifically designed to evaluate the atomic visual perception capabilities of Multimodal Large Language Models (MLLMs). Existing benchmar…
S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence
Yalun Dai, Hao Li, Shulin Tian +8
Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inf…
Apple-: Benchmarking Thinking with Video Towards Law-Grounded Physical Intelligence
Runmao Yao, Kairui Hu, Yukang Cao +11
Modern video generation models are increasingly hailed as emerging world models with an internalized grasp of physical law. Yet existing benchmarks largely evaluate physical plausi…
ViQ: Text-Aligned Visual Quantized Representations at Any Resolution
Xumin Yu, Zuyan Liu, Zhenyu Yang +5
A unified representation for text and vision is a natural pursuit, as it enables simpler multimodal modeling and more efficient training. However, representing images as discrete s…
From Pixels to Words -- Towards Native One-Vision Models at Scale
Haiwen Diao, Jiahao Wang, Penghao Wu +18
Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular framework that inevitably fragmen…
Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16
With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and…
FileGram: Grounding Agent Personalization in File-System Behavioral Traces
Shuai Liu, Shulin Tian, Kairui Hu +6
Coworking AI agents operating within local file systems are rapidly emerging as a paradigm in human-AI interaction; however, effective personalization remains limited by severe dat…
PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning
Shaoxuan Li, Zhixuan Zhao, Hanze Deng +9
We introduce PerceptionComp, a manually annotated benchmark for complex, long-horizon, perception-centric video reasoning. PerceptionComp is designed so that no single moment is su…
VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining
Xuanyu Zhu, Yuhao Dong, Rundong Wang +9
Recent advancements extend Multimodal Large Language Models (MLLMs) beyond standard visual question answering to utilizing external tools for advanced visual tasks. Despite this pr…
Insight-V++: Towards Advanced Long-Chain Visual Reasoning with Multimodal Large Language Models
Yuhao Dong, Zuyan Liu, Shulin Tian +2
Large Language Models (LLMs) have achieved remarkable reliability and advanced capabilities through extended test-time reasoning. However, extending these capabilities to Multi-mod…