PyVision: Agentic Vision with Dynamic Tooling
Authors: name: Shitian Zhao, name: Haoquan Zhang, name: Shaoheng Lin, name: Ming Li, name: Qilong Wu, name: Kaipeng Zhang, name: Chen Wei

LLMs are increasingly deployed as agents, systems capable of planning,
reasoning, and dynamically calling external tools. However, in visual
reasoning, prior approaches largely remain limited by predefined workflows and
static toolsets. In this report, we present PyVision, an interactive,
multi-turn framework that enables MLLMs to autonomously generate, execute, and
refine Python-based tools tailored to the task at hand, unlocking flexible and
interpretable problem-solving. We develop a taxonomy of the tools created by
PyVision and analyze their usage across a diverse set of benchmarks.
Quantitatively, PyVision achieves consistent performance gains, boosting
GPT-4.1 by +7.8% on V* and Claude-4.0-Sonnet by +31.1% on VLMsAreBlind-mini.
These results point to a broader shift: dynamic tooling allows models not just
to use tools, but to invent them, advancing toward more agentic visual
reasoning.