Interpretable Physics Reasoning and Performance Taxonomy in
  Vision-Language Models
As Vision-Language Models (VLMs) grow in sophistication, their ability to
perform reasoning is coming under increasing supervision. While they excel at
many tasks, their grasp of fundamental scientific principles, such as physics,
remains an underexplored frontier. To reflect the advancements in these
capabilities, we introduce a novel and accessible framework designed to
rigorously evaluate VLMs on their understanding of 2D physics. Our framework
features a pragmatic scenario generator that creates a diverse testbed of over
400 problems across four core domains: Projectile Motion, Collision Dynamics,
Mechanics, and Fluid Dynamics. Through comprehensive evaluation of four
state-of-the-art VLMs, we demonstrate a strong correlation between model scale
and reasoning ability, with our top-performing model, Qwen2.5-VL-7B, achieving
an overall score of 0.815. We find that while models excel at formulaic
problems, they struggle significantly with domains requiring abstract spatial
reasoning. By designing this framework, we aim to democratize the study of
scientific reasoning in VLMs and foster deeper insights into their capabilities
and limitations.
68c21e4f29b8ec9932cd08a5
Pranav Pawar
claimed_verified
2025-09-12T16:15:31.529Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65d02414af9670557423df10/5SNGUGEEdkliGKNceo_gS.jpeg
Pranav Pawar
user
prnvpwr2612
68c21e4f29b8ec9932cd08a6
Kavish Shah
claimed_verified
2025-09-15T15:08:59.917Z
67b2f384b023991df10e161a
/avatars/916cd38530039fad610bef19a13d37bd.svg
Kavish Shah
user
kavish17shah
68c21e4f29b8ec9932cd08a7
Akshat Bhalani
claimed_verified
2025-09-13T14:56:36.319Z
68a9c7381860fc7281b6112a
/avatars/a605586074a1942ca317632179b42fc0.svg
ByteMeHarder-404
user
ByteMeHarder-404
68c21e4f29b8ec9932cd08a8
Komal Kasat
claimed_verified
2025-09-15T15:08:57.687Z
689edbb2ee02f0ffbef27199
/avatars/d66e7b1d9f0a0b69e282f91194065074.svg
Komal Kasat
user
komalkasat
68c21e4f29b8ec9932cd08a9
Dev Mittal
claimed_verified
2025-09-15T15:08:51.347Z
67bed60ec4849f7931cca2e7
/avatars/9479d4a7edfdba5300a496e895235655.svg
DEV MITTAL
user
dev2607
68c21e4f29b8ec9932cd08aa
Hadi Gala
claimed_verified
2025-09-15T15:08:54.556Z
6854fca29853c39b3dd703f6
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/gb74FbumwxbuCVIKfEY7N.png
Hadi Gala
user
hadiiigala
68c21e4f29b8ec9932cd08ab
Deepali Patil
68c21e4f29b8ec9932cd08ac
Nikita Raichada
68c21e4f29b8ec9932cd08ad
Monali Deshmukh
Interpretable Physics Reasoning and Performance Taxonomy in
  Vision-Language Models
As Vision-Language Models (VLMs) grow in sophistication, their ability to
perform reasoning is coming under increasing supervision. While they excel at
many tasks, their grasp of fundamental scientific principles, such as physics,
remains an underexplored frontier. To reflect the advancements in these
capabilities, we introduce a novel and accessible framework designed to
rigorously evaluate VLMs on their understanding of 2D physics. Our framework
features a pragmatic scenario generator that creates a diverse testbed of over
400 problems across four core domains: Projectile Motion, Collision Dynamics,
Mechanics, and Fluid Dynamics. Through comprehensive evaluation of four
state-of-the-art VLMs, we demonstrate a strong correlation between model scale
and reasoning ability, with our top-performing model, Qwen2.5-VL-7B, achieving
an overall score of 0.815. We find that while models excel at formulaic
problems, they struggle significantly with domains requiring abstract spatial
reasoning. By designing this framework, we aim to democratize the study of
scientific reasoning in VLMs and foster deeper insights into their capabilities
and limitations.
A novel framework evaluates Vision-Language Models on 2D physics understanding, revealing a correlation between model scale and reasoning ability, particularly highlighting challenges in abstract spatial reasoning.
Vision-Language Models VLMs scenario generator Projectile Motion Collision Dynamics Mechanics Fluid Dynamics Qwen2.5-VL-7B formulaic problems abstract spatial reasoning