🤗 Upvotes: 66 | cs.CL

Authors:
Guijin Son, Seungone Kim, Catherine Arnett, Hyunwoo Ko, Hyein Lee, Hyeonah Kang, Jiang Longxi, Jin Yun, JungYup Lee, Kyungmin Lee, Sam Yoosuk Kim, Sang Park, Seunghyeok Hong, SeungJae Lee, Seungyeop Yi, Shinae Shin, SunHye Bok, Sunyoung Shin, Yonghoon Ji, Youngtaek Kim, Hanearl Jung, Akari Asai, Graham Neubig, Sean Welleck, Youngjae Yu, Akshelin R, Alexander B. Ivanov, Boboev Muhammadjon, Chaeyoung Han, Christian Stump, Dmitrii Karp, Dohyun Kwon, DoYong Kwon, Duk-Soon Oh, Giovanni Resta, Greta Panova, Huiyun Noh, Hyungryul Baik, Hyungsun Bae, Inomov Mashrafdzhon, Jeewon Kim, Ji Eun Lee, Jiaqi Liu, Jieui Kang, Jimin Kim, Jon-Lark Kim, Junseo Yoon, Junwoo Jo, Kibeom Kim, Kiwoon Kwon, Mario Kummer, Max Mercer, Minjun Kim, Nahyun Lee, Ng Ze-An, Rafał Marcin Łochowski, Raphaël Lachièze-Rey, Ruichen Zhang, Sejin Park, Seonguk Seo, Shin Jaehoon, Sunatullo, Taewoong Eom, Yeachan Park, Yongseok Jang, Youchan Oh, Zhaoyang Wang, Zoltán Kovács

Title:
Soohak: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs

Arxiv:
http://arxiv.org/abs/2605.09063v1

Abstract:
Following the recent achievement of gold-medal performance on the IMO by frontier LLMs, the community is searching for the next meaningful and challenging target for measuring LLM reasoning. Whereas olympiad-style problems measure step-by-step reasoning alone, research-level problems use such reasoning to advance the frontier of mathematical knowledge itself, emerging as a compelling alternative. Yet research-level math benchmarks remain scarce because such problems are difficult to source (e.g., Riemann Bench and FrontierMath-Tier 4 contain 25 and 50 problems, respectively). To support reliable evaluation of next-generation frontier models, we introduce Soohak, a 439-problem benchmark newly authored from scratch by 64 mathematicians. Soohak comprises two subsets. On the Challenge subset, frontier models including Gemini-3-Pro, GPT-5, and Claude-Opus-4.5 reach 30.4%, 26.4%, and 10.4% respectively, leaving substantial headroom, while leading open-weight models such as Qwen3-235B, GPT-OSS-120B, and Kimi-2.5 remain below 15%. Notably, beyond standard problem solving, Soohak introduces a refusal subset that probes a capability intrinsic to research mathematics: recognizing ill-posed problems and pausing rather than producing confident but unjustified answers. On this subset, no model exceeds 50%, identifying refusal as a new optimization target that current models do not directly address. To prevent contamination, the dataset will be publicly released in late 2026, with model evaluations available upon request in the interim.