PhysReason

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<step_1: string, step_2: string, step_3: string, step_4: string, step_5: string>
to
{'step_1': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<step_1: string, step_2: string, step_3: string, step_4: string, step_5: string>
              to
              {'step_1': Value('string')}

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PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning

PhysReason is accepted by ACL-2025-main

📋 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. This benchmark addresses the critical gap in evaluating large language models' capabilities in physics-based reasoning, which requires applying physics theorems and constraints in complex problem-solving scenarios.

✨ Key Features

📊 Dataset Size: 1,200 carefully curated physics problems
🎯 Problem Types: Strategic mix of knowledge-based (25%) and reasoning-based (75%) questions
📚 Theorem Coverage: Comprehensive coverage of 147 physics theorems
🎨 Visual Content: 81% of problems include diagrams and visual elements
📈 Difficulty Levels: Four distinct levels - Knowledge, Easy, Medium, Hard
🔄 Step-by-step Solutions: Average of 8.1 solution steps per problem (15.6 for hard problems)
🌍 Multi-modal: Supports both text and image inputs

🔧 Data Collection

Our rigorous data collection process ensures high-quality, challenging problems:

📖 Sources: Global college entrance exams and international physics competitions
⚙️ Process: Standardized using MinerU framework for consistent formatting
✅ Quality Control: Two-phase translation process with expert verification
🔍 Filtering: Systematically excluded easily searchable problems to prevent data leakage
📊 Classification: Difficulty levels based on solving time and theorem complexity analysis

📊 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

We introduce the Physics Solution Auto Scoring (PSAS) framework with two complementary evaluation approaches:

PSAS-A (Answer Level Evaluation)

Sub-question Assessment: Evaluates answers for each sub-question independently
LLM-based Extraction: Uses advanced language models for answer extraction
Semantic Verification: Ensures semantic consistency between extracted and ground truth answers
Weighted Scoring: Considers solution step lengths as weights for different sub-questions

PSAS-S (Step Level Evaluation)

Provides detailed step-by-step assessment through four phases:

Data Extraction: Parses model responses and reference solutions
Scoring: Evaluates correctness of each reasoning step
First Error Detection: Identifies where models first deviate from correct reasoning
Error Analysis: Classifies error types into four key bottlenecks:
- Physics Theorem Application
- Physics Process Understanding
- Calculation
- Physics Condition Analysis

🚀 Usage

Core Evaluation Files

answer_evaluation_with_ds_ch_prompt.py: Answer-level evaluation using Chinese prompts
answer_evaluation_with_ds_en_prompt.py: Answer-level evaluation using English prompts
format_result_ds.py: Optimizes unstable outputs into stable, consistent formats
step_evaluation_with_ds_ch_prompt.py: Step-level evaluation using Chinese prompts
step_evaluation_with_ds_en_prompt.py: Step-level evaluation using English prompts

📈 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

Performance Gap: Even top-performing models achieve less than 60% on answer-level evaluation
Difficulty Scaling: Performance drops significantly from knowledge questions (75.11%) to hard problems (31.95%)
O-like Model Advantage: Models with enhanced reasoning capabilities show superior performance
Multi-modal Benefits: Visual content significantly enhances model understanding and performance
Four Critical Bottlenecks identified through step-level evaluation:
1. Physics Theorem Application
2. Physics Process Understanding
3. Calculation Accuracy
4. Physics Condition Analysis

📝 Citation

If you find PhysReason useful in your research, please cite our paper:

@article{zhang2025physreason,
  title={Physreason: A comprehensive benchmark towards physics-based reasoning},
  author={Zhang, Xinyu and Dong, Yuxuan and Wu, Yanrui and Huang, Jiaxing and Jia, Chengyou and Fernando, Basura and Shou, Mike Zheng and Zhang, Lingling and Liu, Jun},
  journal={arXiv preprint arXiv:2502.12054},
  year={2025}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

We welcome contributions to PhysReason! Please contact us for more details.

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Paper for jolin621/PhysReason

PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning

Paper • 2502.12054 • Published Feb 17, 2025 • 7