collaborators

23 papers

cs.CL2026

Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence

Xuanle Zhao, Qiushi Sun, Jingyu Xiao +16

While Large Language Models (LLMs) have substantially advanced text-to-code generation, many real programming tasks specify intent through visual artifacts such as screenshots, cha…

cs.CV2026

TreeCUA: Efficiently Scaling GUI Automation with Tree-Structured Verifiable Evolution

Deyang Jiang, Jing Huang, Xuanle Zhao +6

Effectively scaling GUI automation is essential for computer-use agents (CUAs); however, existing work primarily focuses on scaling GUI grounding rather than the more crucial GUI p…

cs.CL2026

Length-Unbiased Sequence Policy Optimization: Revealing and Controlling Response Length Variation in RLVR

Fanfan Liu, Youyang Yin, Peng Shi +3

Recent applications of Reinforcement Learning with Verifiable Rewards (RLVR) to Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated significant success…

cs.CV2026

OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models

Yufeng Zhong, Lei Chen, Xuanle Zhao +7

The development of large vision language models drives the demand for managing, and applying massive amounts of multimodal data, making OCR technology, which extracts information f…

cs.LG2026

Flexible Entropy Control in RLVR with a Gradient-Preserving Perspective

Kun Chen, Peng Shi, Fanfan Liu +4

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a critical method for enhancing the reasoning capabilities of Large Language Models (LLMs). However, continuous…

cs.RO2026

Agentic Reward Modeling: Verifying GUI Agent via Progressive Trajectory-Grounded Interaction

Chaoqun Cui, Jing Huang, Shijing Wang +3

Reinforcement learning with verifiable rewards (RLVR) provides a promising pathway for continuously advancing GUI agents, yet existing reward modeling paradigms face complementary…