VisCurate is a framework and benchmark that curates visual skill libraries by evaluating the actual visual behavior of skills rather than relying on textual metadata. It runs candidate skill pairs on a shared probe battery, assigns one of six behavioral relations, and uses these to safely edit the library. The benchmark introduces controlled defect types, producing 300 reproducible instances, and demonstrates that VisCurate outperforms text-based baselines in detecting false merges and recovering mergeable pairs, while revealing high redundancy in real libraries.
arXiv:2607. 01709v1 Announce Type: new Abstract: Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently.
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...
arXiv:2610.28386v1 Announce Type: cross Abstract: Visual tool use enables vision-language models (VLMs) to acquire task-relevant evidence, but its effectiveness can be limited by predefined visual op...
arXiv:2606. 30185v1 Announce Type: new Abstract: Improving vision-language models (VLMs) on visual reasoning typically requires retraining or hand-designed prompts and tools.
arXiv:2610.29241v1 Announce Type: new Abstract: In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in...
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...
arXiv:2608. 08907v1 Announce Type: cross Abstract: Thinking with images allows a multimodal model to compensate for limited perception by invoking visual tools through code.
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:2607. 13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps.
VEvo-Harness is a causally-guarded, continual self‑evolving system designed to improve agentic image generation. It introduces a visual traceback mechanism, visual continual governance, a lifecycle‑managed harness state, and a heterogeneous sandbox policy to ensure only causally valid skill updates are incorporated. The system outperforms existing agentic methods and rivals proprietary generators while preserving continual evolution and generalization.
arXiv:2609.36691v1 Announce Type: new Abstract: Manipulation behaviors vary widely across objects and scenes, but they share a small set of reusable skills, and planning with these skills helps embod...
Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background correlations, resulting in predictions driven by co...