ReGuLaR: Relation-Grounded Latent Reasoning for Large Vision-Language Models 事件

ReGuLaR: Relation-Grounded Latent Reasoning for Large Vision-Language Models arXiv:2605.30587v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has significantly improved the reasoning ability of large vision-language models (LVLMs) by verbalizing intermediate reasoning steps in natural language. However, such discrete textual rationales are often insufficient for encoding continuous visual evidence. Recent work addresses this limitation by moving reasoning into continuous latent