PhyDrawGen: Physically Grounded Diagram Generation from Natural Language
PhyDrawGen is a neuro-symbolic pipeline that generates physically accurate diagrams from text. It uses an LLM to extract a scene graph, a deterministic solver to encode physics constraints, and a fine-tuned Qwen-VL model to iteratively correct violations. Evaluated on 1,449 problems, it outperforms GPT-5-image and Gemini models.
[2605.30512] PhyDrawGen: Physically Grounded Diagram Generation from Natural Language
[Submitted on 28 May 2026]
Title:PhyDrawGen: Physically Grounded Diagram Generation from Natural Language
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Abstract:Generating physics diagrams from text requires strict adherence to physical laws. While current generative models produce visually plausible outputs, they systematically hallucinate force vectors, ignore conservation laws, and violate geometric constraints. We present PhyDrawGen, a neuro-symbolic pipeline that decouples semantic scene understanding from physical constraint satisfaction. First, a large language model extracts a typed scene graph from the problem text. A deterministic solver then converts this graph into a Planar Straight-Line Graph (PSLG), encoding force balance, optical paths, and field topologies as exact geometric primitives. Finally, a fine-tuned Qwen-VL model implements a visually grounded propose-verify loop to iteratively correct any constraint violations. Evaluated on a benchmark of 1,449 problems spanning mechanics, optics, and electromagnetism, PhyDrawGen significantly outperforms GPT-5-image, Gemini 2.5 Flash, and Gemini 3 Pro, demonstrating robust physical accuracy even on unusual-object problems.
Comments: 9 figures, 7 tables. Under review at EMNLP 2026
Subjects:
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.30512 [cs.AI]
(or arXiv:2605.30512v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.30512
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Syed Nazmus Sakib [view email] [v1] Thu, 28 May 2026 19:49:27 UTC (2,781 KB)
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