Datasets:

zhibei1204
/

PhysReason

Tasks:

Question Answering

Languages:

English

ArXiv:

Tags:

physics

License:

Dataset card Files Files and versions Community

Dataset Viewer

Split (1)

train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 826, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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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-T†	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-T*	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-T†	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-T*	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

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