Large language models have learned to call external tools, but in computer vision they still walk a narrow, hand-coded path: crop the image, run a captioner, answer the question—done. PyVision blows up that rut. The 26-page technical report shows GPT-4.1 and Claude-4 Sonnet literally writing Python code mid-conversation, executing it, checking the output and iterating until they solve the task. The result is an agent that treats PIL, NumPy and Matplotlib as an expandable toolbox rather than a fixed pipeline.
From static workflows to dynamic “code-as-tool”
A traditional vision agent might have 10 pre-defined ops; PyVision can spawn hundreds. The authors catalogue the emergent tools into four buckets—basic image processing, advanced processing, visual sketching and numerical analysis—plus a long-tail of creative task-specific snippets. On perception-heavy problems the model leans on cropping and contrast boosts; on math puzzles it sketches diagrams or counts pixels.
Multi-turn loop under the hood
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System prompt primes the LLM to plan, code, run and reflect.
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Python sandbox executes each snippet and streams results back.
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Reflection step lets the model critique outputs, revise code or answer.
The dance repeats until the agent is confident—or it times out. Crucially, there’s no fixed library list; the model imports what it thinks it needs.
Benchmarks: big wins, bigger where it hurts most
| Backend | MathVista ↑ | Visual-Puzzles ↑ | V* ↑ | VLMsAreBlind-mini ↑ |
|---|---|---|---|---|
| GPT-4.1 | +1.8 | +2.5 | +7.8 | +2.6 |
| Claude-4 Sonnet | +3.3 | +8.3 | +0.3 | +31.1 |
Why this matters
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Grounded answers, verifiable steps. The agent surfaces intermediate plots, masks and arrays, giving product teams a check-pointable audit trail.
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Amplifier, not crutch. PyVision “dials up” whatever the base model is best at—perception for GPT-4.1, abstract reasoning for Claude-4—rather than papering over weaknesses.
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Tool invention is the new frontier. Instead of waiting for human engineers to wire in functions, the LLM autogenerates them, inching closer to Benjamin Franklin’s “tool-making animal.”
What’s next
The paper’s GitHub repo ships inference code, a dockerised Python sandbox and demo notebooks. The authors hint at plugging reinforcement learning into the loop and expanding beyond vision to 3-D geometry and web interaction tooling. Expect startups to wrap this framework into agents that can diagnose X-ray anomalies, audit engineering schematics or spot product-label defects—without a human ever defining “defect detector.”
Paper link: arXiv 2507.07998 (PDF)
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