Hidden in Plain Sight: Visual-to-Symbolic Analytical Solution Inference from Field Visualizations
Researchers have introduced ViSA-R2, an AI model capable of inferring analytical physical solutions directly from field visualizations. By mimicking a physicist's reasoning process, it outperforms existing models in solving 2D steady-state field problems.
Computer Science > Artificial Intelligence
Title:Hidden in Plain Sight: Visual-to-Symbolic Analytical Solution Inference from Field Visualizations
View PDF HTML (experimental)Abstract:Recovering analytical solutions of physical fields from visual observations is a fundamental yet underexplored capability for AI-assisted scientific reasoning. We study visual-to-symbolic analytical solution inference (ViSA) for two-dimensional linear steady-state fields: given field visualizations (and first-order derivatives) plus minimal auxiliary metadata, the model must output a single executable SymPy expression with fully instantiated numeric constants. We introduce ViSA-R2 and align it with a self-verifying, solution-centric chain-of-thought pipeline that follows a physicist-like pathway: structural pattern recognition solution-family (ansatz) hypothesis parameter derivation consistency verification. We also release ViSA-Bench, a VLM-ready synthetic benchmark covering 30 linear steady-state scenarios with verifiable analytical/symbolic annotations, and evaluate predictions by numerical accuracy, expression-structure similarity, and character-level accuracy. Using an 8B open-weight Qwen3-VL backbone, ViSA-R2 outperforms strong open-source baselines and the evaluated closed-source frontier VLMs under a standardized protocol.
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Source: arXiv cs.AI Recent
