Showing cs.CVShow all
2 papers · 1 filter
cs.CV2026
DUEL: Adversarial Self-Play for Multimodal Reasoning
Lin Qiu, Hanqing Zeng, Yao Liu +3
Reinforcement learning (RL) has emerged as an effective paradigm for improving the reasoning capability of vision-language models (VLMs). However, RL-based optimization typically d…
cs.CV2026
Ground What You See: Hallucination-Resistant MLLMs via Caption Feedback, Diversity-Aware Sampling, and Conflict Regularization
Miao Pan, Wangjie Gan, Jintao Chen +4
While Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse tasks, their practical deployment is severely hindered by hallucination issues, which…