3 papers
cs.CV2026
Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding
Boqi Chen, Xudong Liu, Jianing Qiu
We study object hallucination in Multimodal Large Language Models (MLLMs) and improve visual contrastive decoding (VCD) by constructing an object-aligned auxiliary view. We leverag…
cs.CV2026
SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models
Weijiang Lv, Yaoxuan Feng, Xiaobo Xia +4
Chain-of-Thought reasoning is widely used to improve the interpretability of multimodal large language models (MLLMs), yet the faithfulness of the generated reasoning traces remain…
cs.LG2025
WeChat-YATT: A Scalable, Simple, Efficient, and Production Ready Training Library
Junyu Wu, Weiming Chang, Xiaotao Liu +10
Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent paradigm for training large language models and multimodal systems. Despite the notable advances enable…