Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gating 事件

Mitigating Hallucinations in Large Vision-Language Models via Causal Route Gating arXiv:2605.24024v1 Announce Type: new Abstract: Large vision-language models (LVLMs) often hallucinate content that is fluent yet unsupported by the image, limiting their reliability in real-world deployment. We show that a key failure mode arises from route competition: even when visual tokens receive attention, the final token decision can be dominated by the textual pathway, causing the decoder to follow lingui