collaborators

8 papers

cs.CV2026

ELDiff: When Evidential Learning Meets Text-to-Image Diffusion

Qingtao Pan, Kai Ye, Zhihao Dou +2

In multi-object text-to-image (T2I) diffusion, ensuring semantic consistency between textual prompts and generated visual content is crucial for image synthesis. However, such cons…

cs.AI2026

STRIDE: Strategic Trajectory Reasoning via Discriminative Estimation for Verifiable Reinforcement Learning

Qinjian Zhao, Zhihao Dou, Dinggen Zhang +10

Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training paradigm for improving the reasoning abilities of large language models. However, existi…

cs.CV2026

CPS4: Class Prompt driven Semi-Supervised Spine Segmentation with Class-specific Consistency Constraint

Qingtao Pan, Hongzan Sun, Bing Ji +1

Vision Language Model (VLM) has great potential to enhance the quality of pseudo labels in semi-supervised spine segmentation by leveraging textual class prompts to generate segmen…

cs.AI2026

Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning

Zhihao Dou, Qinjian Zhao, Zhongwei Wan +10

Large language models (LLMs) demonstrate strong reasoning abilities via Chain-of-Thought (CoT), but their token-level generation encourages local decisions and lacks global plannin…

cs.LG2026

Conditional Factuality Controlled LLMs with Generalization Certificates via Conformal Sampling

Kai Ye, Qingtao Pan, Shuo Li

Large language models (LLMs) need reliable test-time control of hallucinations. Existing conformal methods for LLMs typically provide only \emph{marginal} guarantees and rely on a…

cs.CV2026

Frequency-Modulated Visual Restoration for Matryoshka Large Multimodal Models

Qingtao Pan, Zhihao Dou, Shuo Li

Large Multimodal Models (LMMs) struggle to adapt varying computational budgets due to numerous visual tokens. Previous methods attempted to reduce the number of visual tokens befor…