collaborators

22 papers

cs.CV2026

Thinking in Video: Can Video Generators Really Reason About the Real World?

Yongheng Zhang, Guang Yang, Ruihan Hou +12

Recent advances in world models and video generation have given rise to an emerging reasoning paradigm that leverages video generative models to simulate, predict, and reason about…

cs.CL2026

Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks

Wenbo Pan, Jie Xu, Qiguang Chen +5

Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal, is crucial for factual reliability, w…

cs.CV2026

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model

Qiguang Chen, Chengyu Luan, Jiajun Wu +7

Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks f…

cs.CL2026

Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework

Chenyuan Zhang, Qiguang Chen, Xie Chen +6

Cross-lingual chain-of-thought (XCoT) with self-consistency markedly enhances multilingual reasoning, yet existing methods remain costly due to extensive sampling of full trajector…

cs.CV2026

Let's Think with Images Efficiently! An Interleaved-Modal Chain-of-Thought Reasoning Framework with Dynamic and Precise Visual Thoughts

Xu Liu, Yongheng Zhang, Qiguang Chen +3

Recently, Interleaved-modal Chain-of-Thought (ICoT) reasoning has achieved remarkable success by leveraging both multimodal inputs and outputs, attracting increasing attention. Whi…

cs.CL2026

Learning the Boundary of Solvability: Aligning LLMs to Detect Unsolvable Problems

Dengyun Peng, Qiguang Chen, Bofei Liu +6

Ensuring large language model (LLM) reliability requires distinguishing objective unsolvability (inherent contradictions) from subjective capability limitations (tasks exceeding mo…