ArxivRollBench-Full

Overview

ArxivRollBench is a rolling benchmark built from recent arXiv papers. It evaluates whether large language models can reason over fresh scientific text through three task formats: sequencing, cloze, and next-fragment prediction.

Task Description

• Task Type: Multiple-choice scientific text reasoning

• Input: Recent arXiv text fragments with four answer choices

• Output: Single correct answer letter (A, B, C, or D)

• Domains: Computer Science, Quantitative Finance, Mathematics, Physics, Statistics, Quantitative Biology, Economics, and Electrical Engineering/System Science

• Releases: 2024b, 2025a, and 2026a rolling snapshots

Key Features

• Time-aware benchmark snapshots reduce contamination-related overestimation

• Covers multiple arXiv domains and scientific writing styles

• Includes sequencing, cloze, and prediction formats under the SCP framework

• Compact -50 split is suitable for cost-controlled API evaluation

• Full split is available as arxivrollbench_full

Evaluation Notes

• Default configuration uses 0-shot evaluation

• The default arxivrollbench benchmark uses compact -50 datasets

• Use arxivrollbench_full for the complete public splits

• Each subset is loaded from the public ModelScope mirror under the liangzid namespace

• Answers are normalized to A-D and evaluated with accuracy

Properties

Property	Value
Benchmark Name	arxivrollbench_full
Dataset ID	liangzid/arxivrollbench-full
Paper	Paper
Tags	Knowledge, MCQ, Reasoning
Metrics	acc
Default Shots	0-shot
Evaluation Split	train

Data Statistics

Metric	Value
Total Samples	245,433
Prompt Length (Mean)	1499.93 chars
Prompt Length (Min/Max)	307 / 28864 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
2024b_cs_s	2,931	962.16	574	4774
2024b_cs_c	2,377	307	307	307
2024b_cs_p	3,166	2663.27	793	10327
2024b_q_fin_s	852	1026.01	574	3549
2024b_q_fin_c	747	307	307	307
2024b_q_fin_p	881	3207.96	793	16189
2024b_math_s	2,107	886.2	574	3466
2024b_math_c	1,238	307	307	307
2024b_math_p	2,532	2295.3	793	11911
2024b_physics_s	1,966	984.28	575	4225
2024b_physics_c	1,482	307	307	307
2024b_physics_p	2,141	3166.87	793	28864
2024b_stat_s	3,482	985.03	574	6098
2024b_stat_c	2,800	307	307	307
2024b_stat_p	3,704	3000.94	793	15321
2024b_q_bio_s	1,485	1039.14	574	3895
2024b_q_bio_c	1,318	307	307	307
2024b_q_bio_p	1,550	3332.41	804	16126
2024b_econ_s	879	1023.84	576	3421
2024b_econ_c	764	307	307	307
2024b_econ_p	919	3176.67	851	15040
2024b_eess_s	3,771	1014.36	574	4356
2024b_eess_c	3,278	307	307	307
2024b_eess_p	3,976	3048.85	793	17290
2025a_cs_s	12,806	981.57	574	5696
2025a_cs_c	11,244	307	307	307
2025a_cs_p	13,331	2823.48	793	20389
2025a_q_fin_s	851	1013.21	576	2609
2025a_q_fin_c	758	307	307	307
2025a_q_fin_p	884	3128.37	793	13025
2025a_math_s	10,362	908.79	574	6001
2025a_math_c	6,344	307	307	307
2025a_math_p	12,145	2444.85	793	12037
2025a_physics_s	10,696	1002.06	574	4761
2025a_physics_c	8,358	307	307	307
2025a_physics_p	11,595	3369.68	793	25245
2025a_stat_s	5,288	985.58	574	8627
2025a_stat_c	4,285	307	307	307
2025a_stat_p	5,589	2935.37	793	15676
2025a_q_bio_s	1,598	1043.55	574	3115
2025a_q_bio_c	1,443	307	307	307
2025a_q_bio_p	1,669	3370.82	796	18074
2025a_econ_s	951	998.31	574	2900
2025a_econ_c	827	307	307	307
2025a_econ_p	982	3176.93	793	11038
2025a_eess_s	8,171	1011.86	574	3844
2025a_eess_c	7,155	307	307	307
2025a_eess_p	8,577	3042.87	793	18934
2026a_cs_s	1,857	981.82	574	3532
2026a_cs_c	1,648	307	307	307
2026a_cs_p	1,933	2724.96	814	11328
2026a_q_fin_s	986	985.79	574	2961
2026a_q_fin_c	886	307	307	307
2026a_q_fin_p	1,046	2727.72	802	10072
2026a_math_s	2,435	869.86	574	3795
2026a_math_c	1,600	307	307	307
2026a_math_p	2,777	1953.57	808	12053
2026a_physics_s	1,863	1007.76	574	3813
2026a_physics_c	1,575	307	307	307
2026a_physics_p	2,019	3072.96	798	13540
2026a_stat_s	3,126	964.56	574	3136
2026a_stat_c	2,627	307	307	307
2026a_stat_p	3,322	2549.38	814	10028
2026a_q_bio_s	1,502	1020.61	574	3281
2026a_q_bio_c	1,373	307	307	307
2026a_q_bio_p	1,569	3074.52	806	11848
2026a_econ_s	914	995.97	574	3043
2026a_econ_c	828	307	307	307
2026a_econ_p	973	2858.55	818	11577
2026a_eess_s	4,200	1006.27	574	3698
2026a_eess_c	3,710	307	307	307
2026a_eess_p	4,409	2790.21	817	13794

Sample Example

Subset: 2024b_cs_s

{
  "input": [
    {
      "id": "509c2daa",
      "content": "Answer the following ArxivRollBench multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nSelect the option that correctly compl ... [TRUNCATED 283 chars] ... rators can be used directly to verify representations of classical groups [12].\n**C**: In practice it is the generating set produced by the constructive recognition algorithms from [10, 11] as implemented in MAGMA\n\nA) CAB\nB) ACB\nC) BAC\nD) CAB"
    }
  ],
  "choices": [
    "CAB",
    "ACB",
    "BAC",
    "CAB"
  ],
  "target": "B",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "original_label": "Selection 2",
    "task_type": "s/c"
  }
}

Prompt Template

Prompt Template:

Answer the following ArxivRollBench multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.

{question}

{choices}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets arxivrollbench_full \
    --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=['arxivrollbench_full'],
    dataset_args={
        'arxivrollbench_full': {
            # subset_list: ['2024b_cs_s', '2024b_cs_c', '2024b_cs_p']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)