arXiv:2606. 30185v1 Announce Type: new Abstract: Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools.
By Yutao Sun, Yanting Miao, Hao-Xuan Ma, Mengyu Zhou, Mingshuai Chen, Tiancheng Zhao, Dexin Wang, Lei Lv, Li Xu, Xiaoxi Jiang, Guanjun Jiang
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 adapts a frozen VLM without any weight updates.
arXiv:2610.14403v2 Announce Type: replace
Abstract: Learning reusable skills can improve visual reasoning agents, yet applying a skill still requires the agent to implement or invoke a procedure and...
By Jiaqi Liu, Ruinan Jin, Xiaoxiao Li
arXiv:2607. 12815v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting is widely used as a test-time scaling strategy for Vision-Language Models (VLMs), but it remains unclear what is extended when VLMs generate longer reasoning traces.
By Hiroto Osaka, Shohei Taniguchi, Gouki Minegishi, Kai Yamashita, Masahiro Suzuki, Yutaka Matsuo
T3R is a training‑free, two‑stage token refinement framework designed to improve the efficiency and robustness of Vision‑Language‑Action (VLA) models. The first stage uses segmentation‑guided pruning to remove irrelevant visual patches, while the second stage applies saliency‑guided attention biasing to focus on action‑critical regions and language cues. Experiments on LIBERO‑Spatial, LIBERO, SIMPLER, and RoboTwin show that T3R can eliminate 85% of visual tokens with minimal loss in success rate and even improve performance under distribution shifts, achieving up to a 3.3× speedup in large‑scale settings.
By Mohammed Hassan (Celine), Zhenyang Chen (Celine), Zheng Wang (Celine), Zhixin Zhu (Celine), Tianlong Chen (Celine), Yingyan (Celine), Lin, Chaojian Li
arXiv:2609.24362v1 Announce Type: new
Abstract: Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language m...
By Hexiong Yang, Mingrui Chen, Jie Cao, Ran He