arXiv AI
VisualEvolve: A Skill Self-Evolution Framework for Visual Reasoning
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arXiv AI
Jun 30
Dynamo: Dynamic Skill-Tool Evolution for Vision-Language Agents
arXiv:2606. 30185v1 Announce Type: new Abstract: Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools.
Hugging Face Trending Papers
Jun 29
Dynamo: Dynamic Skill-Tool Evolution for Vision-Language Agents
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 AI
Oct 9
VICO: Visual Environments Co-Evolving for Vision-Language Model Reasoning
VICO proposes a co‑evolutionary framework where a vision‑language model (actor) and an Environment‑as‑Rewriter (EnvRewriter) are trained together. The EnvRewriter edits visual structures—such as scene graphs, chart tables, or protected region masks—to generate new training samples whose difficulty is calibrated to the actor’s current performance via a pass‑rate reward. Across nine multimodal benchmarks, VICO‑8B outperforms its base model by up to +5.0 % on out‑of‑domain tasks and surpasses other co‑evolution baselines while requiring far fewer labeled samples.
arXiv Computation and Language
Aug 21
SPyCE: Skill-Policy Co-evolution for Multimodal Agents
arXiv:2607. 13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps.
arXiv Machine Learning
Aug 27
VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning
VBVR-Pro is a closed‑loop testbed that enables native visual reasoning through generation, offering 300 procedurally generated tasks that scale training and allow strong transfer to external benchmarks. It supplies verifiable reward scorers based on deterministic, task‑specific rules, outperforming VLM‑as‑a‑judge approaches and providing reliable signals for reinforcement learning. The suite also facilitates controlled modality studies, revealing that video generation excels at persistent spatiotemporal tracking while interleaved generation offers a compute‑efficient alternative, and highlights the importance of vision‑native trajectories for reasoning.
arXiv Computer Vision
Oct 1
CoEvoWhen: Policy-Tool Coevolution for Ultra-Long Video Temporal Grounding
arXiv:2609.40048v1 Announce Type: new Abstract: Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget,...