Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark
Authors
Minhui Zhu
Minyang Tian
Xiaocheng Yang
Tianci Zhou
Lifan Yuan
Penghao Zhu
Eli Chertkov
Shengyan Liu
Yufeng Du
Ziming Ji
Indranil Das
Junyi Cao
Yufeng Du
Jiabin Yu
Peixue Wu
Jinchen He
Yifan Su
Yikun Jiang
Yujie Zhang
Chang Liu
Ze-Min Huang
Weizhen Jia
Yunkai Wang
Farshid Jafarpour
Yong Zhao
Xinan Chen
Jessie Shelton
Aaron W. Young
John Bartolotta
Wenchao Xu
Yue Sun
Anjun Chu
Victor Colussi
Chris Akers
Nathan Brooks
Wenbo Fu
Jinchao Zhao
Marvin Qi
Anqi Mu
Yubo Yang
Allen Zang
Yang Lyu
Peizhi Mai
Christopher Wilson
Xuefei Guo
Juntai Zhou
Daniel Inafuku
Chi Xue
Luyu Gao
Ze Yang
Yaïr Hein
Yonatan Kahn
Kevin Zhou
Di Luo
John Drew Wilson
Jarrod T. Reilly
Dmytro Bandak
Ofir Press
Liang Yang
Xueying Wang
Hao Tong
Nicolas Chia
Eliu Huerta
Hao Peng
Abstract
While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, open-ended challenges found in frontier physics research? And crucially, what kinds of reasoning tasks do physicists want LLMs to assist with? To address these questions, we present the CritPt (Complex Research using Integrated Thinking - Physics Test, pronounced "critical point"), the first benchmark designed to test LLMs on unpublished, research-level reasoning tasks that broadly covers modern physics research areas, including condensed matter, quantum physics, atomic, molecular & optical physics, astrophysics, high energy physics, mathematical physics, statistical physics, nuclear physics, nonlinear dynamics, fluid dynamics and biophysics. CritPt consists of 71 composite research challenges designed to simulate full-scale research projects at the entry level, which are also decomposed to 190 simpler checkpoint tasks for more fine-grained insights. All problems are newly created by 50+ active physics researchers based on their own research. Every problem is hand-curated to admit a guess-resistant and machine-verifiable answer and is evaluated by an automated grading pipeline heavily customized for advanced physics-specific output formats. We find that while current state-of-the-art LLMs show early promise on isolated checkpoints, they remain far from being able to reliably solve full research-scale challenges: the best average accuracy among base models is only 5.7%, achieved by GPT-5 (high), moderately rising to around 10% when equipped with coding tools. Through the realistic yet standardized evaluation offered by CritPt, we highlight a large disconnect between current model capabilities and realistic physics research demands, offering a foundation to guide the development of scientifically grounded AI tools.
