AI RESEARCH
Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains
arXiv CS.CV
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ArXi:2602.13235v2 Announce Type: replace-cross Visual Retrieval-Augmented Generation (VRAG) enhances Vision-Language Models (VLMs) by incorporating external visual documents to address a given query. Existing VRAG frameworks usually depend on rigid, pre-defined external tools to extend the perceptual capabilities of VLMs, typically by explicitly separating visual perception from subsequent reasoning processes. However, this decoupled design can lead to unnecessary loss of visual information, particularly when image-based operations such as cropping are applied.