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) [📝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	✅	295	COL	OE	80.5	❌	315.9	2.8
MMMU	✅	443	COL	OE,MC	53.8	-	-	-
ScienceQA	✅	617	K1-K12	MC	13.3	❌	63.0	2.4
OlympiadBench	✅	2334	COMP	OE	222.0	❌	199.8	3.7
EMMA	✅	156	-	MC	109.5	-	-	-
Ours-Knowledge	✅	300	CEE+COMP	OE	163.7	✅	196.5	3.3
Ours-Easy	✅	300	CEE+COMP	OE	171.2	✅	241.5	5.0
Ours-Medium	✅	300	CEE+COMP	OE	229.2	✅	391.3	8.4
Ours-Hard	✅	300	CEE+COMP	OE	340.9	✅	936.1	15.6
Ours-Full	✅	1200	CEE+COMP	OE	226.3	✅	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