evalstone/evalscope/docs/en/benchmarks/phyx_mc.md

PhyX-MC

Overview

PhyX is the first large-scale benchmark for physical reasoning in realistic, visually grounded scenarios. This is its multiple-choice variant: each university-level physics problem is presented with a figure and four answer options, and the model has to name the correct option letter.

Task Description

• Task Type: Visual multiple-choice physics problem solving
• Input: A figure plus the problem description, question and four labelled options
• Output: A single option letter (A, B, C or D)
• Domain: University-level physics (mechanics, electromagnetism, thermodynamics, wave/acoustics, optics, modern physics)

Key Features

• 3,000 university-level problems (test) over 6 core domains and 25 sub-domains, each domain exposed as its own subset; eval_split='test_mini' selects the official 1,000-problem testmini set.

• Every problem is grounded in a figure that carries information the text does not restate, so the model must combine visual cues with implicit physical laws.

• 6 reasoning types are represented (physical model grounding, multi-formula, spatial relation, numerical, predictive and implicit condition reasoning).

• Uses the default Text-DeRedundancy input style of the paper: the simplified problem description plus the question, with the figure attached.

• The official prompt is reproduced verbatim, including its instruction to answer with the option letter only, so scores stay comparable with the published numbers.

Evaluation Notes

• Primary metric: acc, mean over problems, reported overall and per domain.
• Default scoring is the official string-level match: the chosen letter is extracted from the reply and compared with the ground truth, accepting replies that mark the correct option the way the prompt prints it (D:) or emphasises it (**D**).
• Setting judge.strategy='llm' with judge.models reproduces the official LLM-judged mode. The judge is only consulted for replies whose option letter could not be extracted, matching upstream.
• Figures are sent inline as base64 and the largest is ~5 MB; set max_image_bytes in dataset_args if the served model enforces a smaller per-image limit.
• Resources: Paper | GitHub | Project page

Properties

Property	Value
Benchmark Name	phyx_mc
Dataset ID	evalscope/PhyX
Paper	Paper
Tags	MCQ, MultiModal, Reasoning
Metrics	accuracy
Default Shots	0-shot
Evaluation Split	test

Data Statistics

Metric	Value
Total Samples	3,000
Prompt Length (Mean)	487.19 chars
Prompt Length (Min/Max)	178 / 2039 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
mechanics	550	471.63	203	1364
electromagnetism	550	466.88	189	1125
thermodynamics	500	498.81	178	1283
waves_acoustics	500	492.87	196	1880
optics	500	478.61	194	1376
modern_physics	400	525.59	199	2039

Image Statistics:

Metric	Value
Total Images	3,000
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	215x46 - 5712x4953
Formats	jpeg, png

Sample Example

Subset: mechanics

{
  "input": [
    {
      "id": "4334f3a0",
      "content": [
        {
          "image": "[BASE64_IMAGE: png, ~35.6KB]"
        },
        {
          "text": "A patient with a dislocated shoulder is put into a traction apparatus as shown in figure. The pulls $\\vec{A}$ and $\\vec{B} must combine to produce an outward traction force of 12.8 N on the patient’s arm. How large should these pulls be?Please directly answer the question and provide the correct OPTION LETTER ONLY, e.g., A, B, C, D. OPTION: A: 7.55N B: 5.55N C: 7.65N D: 6.65N"
        }
      ]
    }
  ],
  "target": "A",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "index": "0",
    "category": "Mechanics",
    "subfield": "Statics",
    "reasoning_type": [
      "Spatial Relation Reasoning"
    ]
  }
}

Prompt Template

No prompt template defined.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets phyx_mc \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['phyx_mc'],
    dataset_args={
        'phyx_mc': {
            # subset_list: ['mechanics', 'electromagnetism', 'thermodynamics']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)