Topic
vlm
New Research Reveals How Visual Tokens Evolve Inside Vision-Language Models
A new computer vision paper from arXiv investigates how visual tokens are integrated into large language models (LLMs) under two paradigms: in-context prompting and layer-wise injection. The authors find that visual tokens enter the LLM as 'disguised visual context' lacking linguistic structure, then evolve differently depending on the integration architecture. They show that attention allocation alone is insufficient, and performance depends on the quality of visual representations at each layer.
New AI Research Shows Vision-Language Models Think Better with Visual Grounding
Researchers introduce visually grounded thinking, a reasoning process that interleaves natural-language thoughts with explicit point or box groundings to image regions. The method, using a scalable synthesis pipeline and grounding-aware reinforcement learning, consistently improves performance of Gemma3-4B-IT on counting and spatial reasoning benchmarks, with the 4B model matching or surpassing the 27B variant.