3 papers
cs.CV2026
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Xingyu Zhu, Junfeng Fang, Shuo Wang +4
Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated dat…
cs.SE2026
CodeCircuit: Toward Inferring LLM-Generated Code Correctness via Attribution Graphs
Yicheng He, Zheng Zhao, Zhou Kaiyu +3
Current paradigms for code verification rely heavily on external mechanisms-such as execution-based unit tests or auxiliary LLM judges-which are often labor-intensive or limited by…
cs.CV2025
VisPlay: Self-Evolving Vision-Language Models from Images
Yicheng He, Chengsong Huang, Zongxia Li +2
Reinforcement learning (RL) provides a principled framework for improving Vision-Language Models (VLMs) on complex reasoning tasks. However, existing RL approaches often rely on hu…