Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula
  Discovery
Automated discovery of physical laws from observational data in the real
world is a grand challenge in AI. Current methods, relying on symbolic
regression or LLMs, are limited to uni-modal data and overlook the rich, visual
phenomenological representations of motion that are indispensable to
physicists. This "sensory deprivation" severely weakens their ability to
interpret the inherent spatio-temporal patterns within dynamic phenomena. To
address this gap, we propose VIPER-R1, a multimodal model that performs Visual
Induction for Physics-based Equation Reasoning to discover fundamental symbolic
formulas. It integrates visual perception, trajectory data, and symbolic
reasoning to emulate the scientific discovery process. The model is trained via
a curriculum of Motion Structure Induction (MSI), using supervised fine-tuning
to interpret kinematic phase portraits and to construct hypotheses guided by a
Causal Chain of Thought (C-CoT), followed by Reward-Guided Symbolic Calibration
(RGSC) to refine the formula structure with reinforcement learning. During
inference, the trained VIPER-R1 acts as an agent: it first posits a
high-confidence symbolic ansatz, then proactively invokes an external symbolic
regression tool to perform Symbolic Residual Realignment (SR^2). This final
step, analogous to a physicist's perturbation analysis, reconciles the
theoretical model with empirical data. To support this research, we introduce
PhysSymbol, a new 5,000-instance multimodal corpus. Experiments show that
VIPER-R1 consistently outperforms state-of-the-art VLM baselines in accuracy
and interpretability, enabling more precise discovery of physical laws. Project
page: https://jiaaqiliu.github.io/VIPER-R1/
68b595ffdc94ad77a7bfcfbb
Jiaqi Liu
claimed_verified
2025-09-08T07:02:35.126Z
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JiaqiLiu
user
JiaaqiLiu
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Songning Lai
68b595ffdc94ad77a7bfcfbd
Pengze Li
68b595ffdc94ad77a7bfcfbe
Di Yu
68b595ffdc94ad77a7bfcfbf
Wenjie Zhou
68b595ffdc94ad77a7bfcfc0
Yiyang Zhou
68b595ffdc94ad77a7bfcfc1
Peng Xia
claimed_verified
2025-11-21T09:03:08.843Z
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Peng Xia
user
richardxp888
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Zijun Wang
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Xi Chen
68b595ffdc94ad77a7bfcfc4
Shixiang Tang
68b595ffdc94ad77a7bfcfc5
Lei Bai
68b595ffdc94ad77a7bfcfc6
Wanli Ouyang
68b595ffdc94ad77a7bfcfc7
Mingyu Ding
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Huaxiu Yao
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Aoran Wang
claimed_verified
2025-09-02T07:56:38.934Z
666839475ddf78d3bc5b80f4
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/j4OBskJ_GMS2QGE_nEuEJ.jpeg
Aoran Wang
user
RalfWang
Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula
  Discovery
Automated discovery of physical laws from observational data in the real
world is a grand challenge in AI. Current methods, relying on symbolic
regression or LLMs, are limited to uni-modal data and overlook the rich, visual
phenomenological representations of motion that are indispensable to
physicists. This "sensory deprivation" severely weakens their ability to
interpret the inherent spatio-temporal patterns within dynamic phenomena. To
address this gap, we propose VIPER-R1, a multimodal model that performs Visual
Induction for Physics-based Equation Reasoning to discover fundamental symbolic
formulas. It integrates visual perception, trajectory data, and symbolic
reasoning to emulate the scientific discovery process. The model is trained via
a curriculum of Motion Structure Induction (MSI), using supervised fine-tuning
to interpret kinematic phase portraits and to construct hypotheses guided by a
Causal Chain of Thought (C-CoT), followed by Reward-Guided Symbolic Calibration
(RGSC) to refine the formula structure with reinforcement learning. During
inference, the trained VIPER-R1 acts as an agent: it first posits a
high-confidence symbolic ansatz, then proactively invokes an external symbolic
regression tool to perform Symbolic Residual Realignment (SR^2). This final
step, analogous to a physicist's perturbation analysis, reconciles the
theoretical model with empirical data. To support this research, we introduce
PhysSymbol, a new 5,000-instance multimodal corpus. Experiments show that
VIPER-R1 consistently outperforms state-of-the-art VLM baselines in accuracy
and interpretability, enabling more precise discovery of physical laws. Project
page: https://jiaaqiliu.github.io/VIPER-R1/
VIPER-R1, a multimodal model combining visual perception, trajectory data, and symbolic reasoning, discovers physical laws with higher accuracy and interpretability than existing methods.
Visual Induction Physics-based Equation Reasoning Motion Structure Induction Causal Chain of Thought Reward-Guided Symbolic Calibration Symbolic Residual Realignment PhysSymbol multimodal model symbolic regression reinforcement learning