1 citations · 1 across the 10 of their papers we have counts for
12 papers
MT-Web2Code: Benchmarking Coding Agents on Multi-Turn Regional Reconstruction and Localized Modification
Qiming Li, Shujie Hu, Haohan Liu +3
Recent advances in Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in web UI generation. However, existing benchmarks predominantly focus on single-t…
PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping
Qiming Li, Tianlun Li, Xiaolong Cheng +5
Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language Models (LVLMs). However, exis…
POINav: Benchmarking and Enhancing Final-Meters Arrival in Real-World Vision-Language Navigation
Ruiyan Gong, Meisheng Zhang, Yuxiang Zhao +12
Real-world navigation is fundamentally driven by Points of Interest (POIs), yet reaching a precise POI remains a critical "final-meters" challenge. Existing Vision-Language Navigat…
CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
Qiming Li, Zekai Ye, Xiaocheng Feng +9
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, l…
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play
Xiachong Feng, Deyi Yin, Xiaocheng Feng +9
Games offer a compelling paradigm for developing general reasoning capabilities in language models, as they naturally demand strategic planning, probabilistic inference, and adapti…
Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models
Zekai Ye, Qiming Li, Xiaocheng Feng +6
While Reinforcement Learning from Verifiable Rewards (RLVR) has advanced reasoning in Large Vision-Language Models (LVLMs), prevailing frameworks suffer from a foundational methodo…
MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents
Ruihan Chen, Qiming Li, Xiaocheng Feng +9
Large Vision-Language Models (LVLMs) have shown strong potential as multilingual Graphical User Interface (GUI) agents, as evidenced by existing GUI benchmarks. However, these benc…
Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation
Qiming Li, Zekai Ye, Xiaocheng Feng +3
Despite the remarkable advancements of Large Vision-Language Models (LVLMs), the mechanistic interpretability remains underexplored. Existing analyses are insufficiently comprehens…
Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering
Qiming Li, Xiaocheng Feng, Yixuan Ma +4
Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) demonstrate strong reasoning capabilities, yet their performance in English significantly outperforms that in…
CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models
Qiming Li, Zekai Ye, Xiaocheng Feng +8
Although Large Vision-Language Models (LVLMs) have demonstrated powerful capabilities in interpreting visual information, they frequently produce content that deviates from visual…
CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention
Zekai Ye, Qiming Li, Xiaocheng Feng +10
Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal abilities but remain prone to multilingual object hallucination, with a higher likelihood of generating…
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
Weihong Zhong, Xiaocheng Feng, Liang Zhao +6
Though advanced in understanding visual information with human languages, Large Vision-Language Models (LVLMs) still suffer from multimodal hallucinations. A natural concern is tha…