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cs.AI2026
Dynamo: Dynamic Skill-Tool Evolution for Vision-Language Agents
Yutao Sun, Yanting Miao, Hao-Xuan Ma +8
Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools. We present Dynamo, a training-free framework that adap…
cs.CV2026
ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
Shen Lin, Jing Lin, Junhao Dong +2
Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affectin…
cs.AI2026
Allegory of the Cave: Measurement-Grounded Vision-Language Learning
Kepeng Xu, Li Xu, Gang He +1
Vision-language models typically reason over post-ISP RGB images, although RGB rendering can clip, suppress, or quantize sensor evidence before inference. We study whether groundin…