PhysReason / README.md
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metadata
task_categories:
  - question-answering
license: mit
language:
  - en
tags:
  - physics

PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning

[[Project Page]](https://dxzxy12138.github.io/PhysReason) [πŸ“Paper]

Currently we only open PhysReason-mini Benchmark

Overview

PhysReason is a comprehensive physics-based reasoning benchmark consisting of 1,200 physics problems spanning multiple domains, with a focus on both knowledge-based (25%) and reasoning-based (75%) questions.

## Key Features
  • Dataset Size: 1,200 problems
  • Problem Types: Mix of knowledge (25%) and reasoning (75%) questions
  • Theorem Coverage: 147 physics theorems
  • Visual Content: 81% problems include diagrams
  • Difficulty Levels: Knowledge, Easy, Medium, Hard

Data Collection

  • Sources: Global college entrance exams and competitions

  • Process: Standardized using MinerU framework

  • Quality Control: Two-phase translation with expert verification

  • Filtering: Excluded easily searchable problems

  • Classification: Based on solving time and theorem complexity

  • Benchmark Comparison

    Benchmark Multi-modal Size Knowledge Question Type Avg. T Step-by-step Avg. T Avg. S
    JEEBench ❌ 123 CEE OE,MC 169.7 - - -
    MMLU-Pro ❌ 1299 COL MC 52.1 - - -
    GPQA ❌ 227 PH.D. OE 111.4 ❌ 197.2 3.6
    SciEval ❌ 1657 - OE,MC 154.5 - - -
    SciBench βœ… 295 COL OE 80.5 ❌ 315.9 2.8
    MMMU βœ… 443 COL OE,MC 53.8 - - -
    ScienceQA βœ… 617 K1-K12 MC 13.3 ❌ 63.0 2.4
    OlympiadBench βœ… 2334 COMP OE 222.0 ❌ 199.8 3.7
    EMMA βœ… 156 - MC 109.5 - - -
    Ours-Knowledge βœ… 300 CEE+COMP OE 163.7 βœ… 196.5 3.3
    Ours-Easy βœ… 300 CEE+COMP OE 171.2 βœ… 241.5 5.0
    Ours-Medium βœ… 300 CEE+COMP OE 229.2 βœ… 391.3 8.4
    Ours-Hard βœ… 300 CEE+COMP OE 340.9 βœ… 936.1 15.6
    Ours-Full βœ… 1200 CEE+COMP OE 226.3 βœ… 441.3 8.1

    Evaluation Framework

    PSAS-A (Answer Level)

    • Evaluates sub-question answers
    • Uses LLM for answer extraction
    • Verifies semantic consistency
    • Weighted scoring based on solution steps

    PSAS-S (Step Level)

    • Four-phase assessment:
      1. Data extraction
      2. Scoring
      3. First error step detection
      4. Error analysis

    Experimental Results

    Non-O-like Models Performance

    Model Input Knowledge Easy Medium Hard Avg.
    Qwen2VL-72B Q, I 41.92/62.47 24.04/45.26 15.97/36.13 4.83/24.23 16.96/42.88
    InternVL2.5-78B Q, I 28.34/64.71 24.16/50.69 17.72/38.56 9.71/25.95 19.98/45.89
    GPT-4o Q, I 50.71/65.82 33.87/51.98 22.73/42.36 11.03/24.71 29.58/47.23
    Deepseek-V3-671B Q, IC 55.86/66.14 40.06/52.77 26.63/44.02 13.73/26.87 34.07/48.42
    Claude-3.5-Sonnet Q, I 54.14/66.45 41.35/55.85 28.14/44.86 15.11/28.51 34.69/49.88
    Gemini-2.0-Flash Q, I 65.08/75.04 54.84/68.60 39.79/55.67 21.99/38.39 45.20/60.40
    Gemini-2.0-Pro Q, I 67.99/79.01 55.43/71.47 44.29/57.74 23.81/42.66 47.88/62.74

    O-like Models Performance

    Model Input Knowledge Easy Medium Hard Avg.
    o1-mini Q, IC 53.90/65.74 35.21/52.26 22.24/40.19 10.61/26.80 30.49/47.18
    QvQ-72B Q, I 62.44/70.92 53.74/64.65 28.18/54.88 14.30/36.47 32.67/57.66
    Gemini-2.0-Flash-Thinking-1206 Q, I 65.35/77.20 51.89/67.49 44.43/58.95 27.14/45.48 47.20/63.07
    QwQ-32B Q, IC 62.03/76.28 54.92/71.08 43.64/62.14 22.99/42.19 45.89/63.87
    GLM-Zero Q, IC 64.95/80.36 54.11/71.54 41.32/63.67 23.04/47.46 46.52/65.76
    o3-mini-high Q, IC 70.67/83.61 67.20/81.95 45.31/64.57 30.12/47.23 53.32/69.34
    Gemini-2.0-Flash-Thinking-0121 Q, I 73.44/84.15 63.17/75.94 50.41/66.60 31.90/48.47 54.73/69.73
    Deepseek-R1 Q, IC 75.11/85.91 65.08/79.81 54.84/72.02 31.95/51.50 56.75/73.26

    PhysReason-mini Results

    Model K. E. M. H. Avg.
    o1-mini 54.80 30.33 15.41 7.92 27.11
    QvQ-72B 51.17 37.10 29.83 22.13 35.06
    QwQ-32B 64.40 50.07 38.88 27.45 45.20
    Gemini-2.0-Flash-Thinking-1206 71.47 49.97 36.83 22.97 45.42
    GLM-Zero 72.70 50.17 43.42 24.70 47.75
    o1 72.47 53.37 49.31 25.32 50.12
    o3-mini-high 71.10 63.20 47.02 31.93 53.31
    Gemini-2.0-Flash-Thinking-0121 76.33 56.87 51.85 32.61 54.42
    Deepseek-R1 85.17 60.77 47.24 33.23 56.60

    Key Findings

    • Strong performance from O-like models
    • Gemini and Deepseek models show competitive results
    • Detailed error analysis through PSAS-S framework
    • Multi-modal capabilities enhance performance
    • Step-by-step evaluation provides deeper insights