ABench-Physics: Benchmarking Physical Reasoning in LLMs via High-Difficulty and Dynamic Physics Problems
ABench-Physics evaluates LLMs' physical reasoning via high-difficulty dynamic problems, revealing significant performance gaps.
Key Findings
Methodology
ABench-Physics consists of two components: Phy_A, a static set of 400 high-difficulty problems, and Phy_B, a dynamic subset of 100 problems with an automatic variation engine to test model robustness under changing conditions. All questions require precise numerical answers with strict formatting and tolerance constraints.
Key Results
- Even the most advanced model, Gemini 2.5 Pro, achieved only 43.0% accuracy on the static problem set Phy_A, highlighting significant gaps in current LLMs' physical reasoning.
- Transitioning from static to dynamic problem sets Phy_B resulted in an average performance drop of 22.5% across all models.
- The rigorous evaluation of dynamic problems shows significant deficiencies in models' adaptability to numerical changes.
Significance
ABench-Physics provides a challenging and diagnostic framework for advancing scientific reasoning, particularly in physical modeling and generalization to dynamic problems. It highlights the limitations of current LLMs in physical reasoning and offers clear directions for future research.
Technical Contribution
ABench-Physics introduces dynamic problem sets and strict numerical evaluation standards, surpassing existing static, multiple-choice benchmarks by offering a more comprehensive assessment of physical reasoning capabilities. It emphasizes models' adaptability under varying conditions rather than mere pattern matching.
Novelty
This is the first benchmark focused on high-difficulty and dynamic physics problems, significantly differing from previous static evaluation methods by emphasizing models' physical modeling capabilities and numerical precision.
Limitations
- Models showed significant performance drops on dynamic problem sets, indicating deficiencies in adapting to numerical changes.
- Current evaluation is limited to numerical calculation problems, not covering other dimensions of physical reasoning.
Future Work
Future research can explore improving LLMs' physical modeling capabilities, particularly in generalizing to dynamic problems, and how these capabilities can be applied to broader scientific fields.
AI Executive Summary
ABench-Physics evaluates large language models (LLMs) in physical reasoning through high-difficulty and dynamic physics problems. Existing benchmarks often fall short due to static and multiple-choice formats, failing to comprehensively assess models' physical modeling abilities. ABench-Physics comprises two components: Phy_A, a static set of 400 high-difficulty problems providing a stable performance baseline, and Phy_B, a dynamic subset of 100 problems with an automatic variation engine to test model robustness under changing conditions.
Experimental results show that even the most advanced models achieved only 43.0% accuracy on the static problem set, with a significant performance drop of 22.5% on dynamic problems. This indicates substantial gaps in current LLMs' physical reasoning, especially in generalizing to dynamic problems.
ABench-Physics offers a challenging and diagnostic framework for advancing scientific reasoning, revealing limitations in current LLMs' physical reasoning and providing clear directions for future research. Future studies can explore improving LLMs' physical modeling capabilities, particularly in generalizing to dynamic problems, and how these capabilities can be applied to broader scientific fields.
Deep Analysis
Background
In recent years, large language models (LLMs) have shown remarkable progress in fields like mathematics and programming, but their capabilities in physics remain underexplored and poorly understood. Physics requires not only precise computation but also deep conceptual understanding and physical modeling skills. Existing benchmarks often fall short due to limited difficulty, multiple-choice formats, and static evaluation settings.
Core Problem
Solving physics problems requires precise computation, deep conceptual understanding, and physical modeling skills. Existing benchmarks often fall short due to limited difficulty, multiple-choice formats, and static evaluation settings, failing to comprehensively assess models' physical modeling abilities.
Innovation
ABench-Physics introduces dynamic problem sets and strict numerical evaluation standards, surpassing existing static, multiple-choice benchmarks by offering a more comprehensive assessment of physical reasoning capabilities. It emphasizes models' adaptability under varying conditions rather than mere pattern matching.
Methodology
- �� Phy_A: 400 static high-difficulty problems providing a stable performance baseline.
- �� Phy_B: 100 dynamic problems with an automatic variation engine generating multiple variants to test model robustness.
- �� Strict numerical evaluation standards requiring precise numerical answers with strict formatting and tolerance constraints.
Experiments
Experiments were conducted on several state-of-the-art LLMs, including Gemini 2.5 Pro and OpenAI models. Evaluations included accuracy on static and dynamic problem sets, focusing on models' adaptability under varying conditions.
Results
Even the most advanced models achieved only 43.0% accuracy on the static problem set, with a significant performance drop of 22.5% on dynamic problems. This indicates substantial gaps in current LLMs' physical reasoning.
Applications
ABench-Physics can be used to evaluate and improve LLMs' performance in physical reasoning, particularly in scientific research and education, helping to develop more robust scientific reasoning capabilities.
Limitations & Outlook
Current evaluation is limited to numerical calculation problems, not covering other dimensions of physical reasoning. Future research can explore improving LLMs' physical modeling capabilities, particularly in generalizing to dynamic problems.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. ABench-Physics is like a complex recipe that requires you not only to know how to chop ingredients but also to understand their properties and how to adjust cooking methods under different conditions. Current models are like chefs who follow steps without understanding, while ABench-Physics tests whether you can still make a delicious dish when ingredients and conditions change.
ELI14 Explained like you're 14
Imagine you're playing a puzzle game. ABench-Physics is like a super hard level that requires you to solve puzzles and still find answers when the puzzles change. Current models are like players who only memorize answers, while ABench-Physics tests if you can still win when the puzzles change.
Glossary
ABench-Physics
A benchmark for evaluating LLMs' physical reasoning capabilities, consisting of static and dynamic problem sets.
Used to test models' physical reasoning under varying conditions.
Phy_A
The static problem set in ABench-Physics, containing 400 high-difficulty problems.
Provides a stable performance baseline for models.
Phy_B
The dynamic problem set in ABench-Physics, containing 100 problems with an automatic variation engine generating variants.
Tests models' robustness under varying conditions.
Dynamic Problem Set
A problem set containing multiple variants to test models' adaptability under varying conditions.
Part of ABench-Physics' Phy_B component.
Numerical Evaluation
Strict numerical calculation standards requiring precise numerical answers with strict formatting and tolerance constraints.
Used to evaluate models' computational accuracy.
Open Questions Unanswered questions from this research
- 1 How to improve models' generalization to dynamic problems remains an unsolved issue.
- 2 Current models' deficiencies in adapting to numerical changes need further research.
Applications
Immediate Applications
Scientific Research
ABench-Physics can be used to evaluate and improve LLMs' physical reasoning capabilities in scientific research.
Long-term Vision
Educational Field
Improving LLMs' physical reasoning capabilities can drive innovation and development in the educational field.
Abstract
Large Language Models (LLMs) have shown impressive performance in domains such as mathematics and programming, yet their capabilities in physics remain underexplored and poorly understood. Physics poses unique challenges that demand not only precise computation but also deep conceptual understanding and physical modeling skills. Existing benchmarks often fall short due to limited difficulty, multiple-choice formats, and static evaluation settings that fail to capture physical modeling ability. In this paper, we introduce ABench-Physics, a novel benchmark designed to rigorously evaluate LLMs' physical reasoning and generalization capabilities. ABench-Physics consists of two components: Phy_A, a static set of 400 graduate- or Olympiad-level problems; and Phy_B, a dynamic subset of 100 problems equipped with an automatic variation engine to test model robustness across changing conditions. All questions require precise numerical answers, with strict formatting and tolerance constraints. Our evaluation of several state-of-the-art LLMs reveals substantial performance gaps, highlighting persistent limitations in physical reasoning, especially in generalization to dynamic variants. ABench-Physics provides a challenging and diagnostic framework for advancing scientific reasoning in LLMs.