arXiv cs.CV
· Papers
In-Context Collapse in Vision-Language Models and How to Mitigate it?
arXiv:2608.02830v1 Announce Type: new Abstract: Many-shot in-context learning (ICL) lets vision-language models (VLMs) adapt from image--label demonstrations without weight updates, and is widely assumed to improve as more demonstrations are supplied. We show the opposite: as demonstrations accumulate, a subset of VLMs