ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning
Hongwei Liu, Junnan Liu, Shudong Liu, Haodong Duan, Yuqiang Li, Mao Su, Xiaohong Liu, Guangtao Zhai, Xinyu Fang, Qianhong Ma, Taolin Zhang, Zihan Ma, Yufeng Zhao, Peiheng Zhou, Linchen Xiao, Wenlong Zhang, Shijie Zhou, Xingjian Ma, Siqi Sun, Jiaye Ge, Meng Li, Yuhong Liu

TL;DR
ATLAS is a comprehensive, high-difficulty, multidisciplinary benchmark designed to evaluate advanced scientific reasoning in large language models, emphasizing originality, cross-disciplinary integration, and complex answer formats.
Contribution
It introduces a large-scale, rigorously curated evaluation suite with novel questions across seven scientific fields, addressing limitations of existing benchmarks.
Findings
Effective in differentiating model reasoning capabilities
Demonstrates the importance of cross-disciplinary evaluation
Highlights the challenge of complex scientific reasoning in LLMs
Abstract
The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Concurrently, existing high-difficulty benchmarks often suffer from narrow disciplinary focus, oversimplified answer formats, and vulnerability to data contamination, creating a fidelity gap with real-world scientific inquiry. To address these challenges, we introduce ATLAS (AGI-Oriented Testbed for Logical Application in Science), a large-scale, high-difficulty, and cross-disciplinary evaluation suite composed of approximately 800 original problems. Developed by domain experts (PhD-level and above), ATLAS spans seven core scientific fields: mathematics, physics, chemistry, biology, computer science, earth science, and materials science. Its key features include: (1) High Originality and Contamination…
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Taxonomy
TopicsTopic Modeling · Scientific Computing and Data Management · Machine Learning in Materials Science
