activity
20242026
collaborators

11 papers

cs.CV2026

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…

cs.AI2026

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…

cs.CL2026

Can Large Language Models Simulate Human Cognition Beyond Behavioral Imitation?

Yuxuan Gu, Lunjun Liu, Xiaocheng Feng +4

An essential problem in artificial intelligence is whether LLMs can simulate human cognition or merely imitate surface-level behaviors, while existing datasets suffer from either s…

cs.AI2026

PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra

Xiachong Feng, Liang Zhao, Weihong Zhong +5

Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human…

cs.CV2025

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…

cs.CV2025

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…