activity
20242026
collaborators

21 papers

cs.CV2026

When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning

Shoubin Yu, Yue Zhang, Zun Wang +4

Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints. Recent…

cs.CV2026

STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models

Yiming Liang, Yixiao Chen, Yiyang Zhou +8

Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-language models (LVLMs) often a…

cs.CV2026

EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding

Ziyang Wang, Yue Zhang, Shoubin Yu +6

Next-generation visual assistants, such as smart glasses, embodied agents, and always-on life-logging systems, must reason over an entire day or more of continuous visual experienc…

cs.CV2026

Ego2Web: A Web Agent Benchmark Grounded in Egocentric Videos

Shoubin Yu, Lei Shu, Antoine Yang +6

Multimodal AI agents are increasingly automating complex real-world workflows that involve online web execution. However, current web-agent benchmarks suffer from a critical limita…

cs.CV2026

VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting

Daeun Lee, Shoubin Yu, Yue Zhang +1

Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still…

cs.CL2026

Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution

Nithin Sivakumaran, Shoubin Yu, Hyunji Lee +4

Chain-of-thought (CoT) reasoning sometimes fails to faithfully reflect the true computation of a large language model (LLM), hampering its utility in explaining how LLMs arrive at…