@inproceedings{zhao-etal-2026-sciencearena,
title = "{S}cience{A}rena: Benchmarking {LLM}s on Latest Scientific Olympiad Competitions",
author = "Zhao, Guangxiang and
Shi, Qilong and
Xiao, Xusen and
Liuwenpu and
Li, Yaoming and
Hao, Linfeng and
Hou, Shuyang and
Guo, Zijian and
Zhang, Xinrui and
Zhao, Yuntian and
Wang, Zhengyang and
Liu, Wenrui and
Wu, Yuhan and
Yang, Tong and
Sun, Lin and
Zhang, Xiangzheng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2658/",
pages = "60506--60523",
ISBN = "979-8-89176-459-0",
abstract = "Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce \textsc{ScienceArena}, an olympiad-style benchmark from thirteen public science competitions in physics, chemistry, and biology, including IPhO and IChO 2025{--}2026, IBO 2023, USAPhO 2026, and USNCO 2025. Its open-ended, multi-step problems use process-credit rubrics, making faithful scoring difficult. We build ScienceArena through an expert-audited digitization pipeline that converts official exams, figures, solutions, and rubrics into structured items verified by olympiad medalists. To scale evaluation beyond costly human grading, we calibrate LLM-as-judge against medalist ground truth on archived answers from five models across IPhO and IChO; two strong judges stay within one point of expert total scores. Medalist notes show that failures often stem from visual grounding, structure fidelity, and global problem control rather than missing terminology. Evaluating fourteen recent LLMs with interleaved solving, we find that top models obtain medal-equivalent rubric scores on several public international exams, while chemistry and long-horizon consistency remain key bottlenecks. We provide an interactive \href{https://science-arena.onrender.com/}{demo}."
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<abstract>Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce ScienceArena, an olympiad-style benchmark from thirteen public science competitions in physics, chemistry, and biology, including IPhO and IChO 2025–2026, IBO 2023, USAPhO 2026, and USNCO 2025. Its open-ended, multi-step problems use process-credit rubrics, making faithful scoring difficult. We build ScienceArena through an expert-audited digitization pipeline that converts official exams, figures, solutions, and rubrics into structured items verified by olympiad medalists. To scale evaluation beyond costly human grading, we calibrate LLM-as-judge against medalist ground truth on archived answers from five models across IPhO and IChO; two strong judges stay within one point of expert total scores. Medalist notes show that failures often stem from visual grounding, structure fidelity, and global problem control rather than missing terminology. Evaluating fourteen recent LLMs with interleaved solving, we find that top models obtain medal-equivalent rubric scores on several public international exams, while chemistry and long-horizon consistency remain key bottlenecks. We provide an interactive \hrefhttps://science-arena.onrender.com/demo.</abstract>
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%0 Conference Proceedings
%T ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions
%A Zhao, Guangxiang
%A Shi, Qilong
%A Xiao, Xusen
%A Li, Yaoming
%A Hao, Linfeng
%A Hou, Shuyang
%A Guo, Zijian
%A Zhang, Xinrui
%A Zhao, Yuntian
%A Wang, Zhengyang
%A Liu, Wenrui
%A Wu, Yuhan
%A Yang, Tong
%A Sun, Lin
%A Zhang, Xiangzheng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Liuwenpu
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhao-etal-2026-sciencearena
%X Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce ScienceArena, an olympiad-style benchmark from thirteen public science competitions in physics, chemistry, and biology, including IPhO and IChO 2025–2026, IBO 2023, USAPhO 2026, and USNCO 2025. Its open-ended, multi-step problems use process-credit rubrics, making faithful scoring difficult. We build ScienceArena through an expert-audited digitization pipeline that converts official exams, figures, solutions, and rubrics into structured items verified by olympiad medalists. To scale evaluation beyond costly human grading, we calibrate LLM-as-judge against medalist ground truth on archived answers from five models across IPhO and IChO; two strong judges stay within one point of expert total scores. Medalist notes show that failures often stem from visual grounding, structure fidelity, and global problem control rather than missing terminology. Evaluating fourteen recent LLMs with interleaved solving, we find that top models obtain medal-equivalent rubric scores on several public international exams, while chemistry and long-horizon consistency remain key bottlenecks. We provide an interactive \hrefhttps://science-arena.onrender.com/demo.
%U https://aclanthology.org/2026.emnlp-main.2658/
%P 60506-60523
Markdown (Informal)
[ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions](https://aclanthology.org/2026.emnlp-main.2658/) (Zhao et al., EMNLP 2026)
ACL
- Guangxiang Zhao, Qilong Shi, Xusen Xiao, Liuwenpu, Yaoming Li, Linfeng Hao, Shuyang Hou, Zijian Guo, Xinrui Zhang, Yuntian Zhao, Zhengyang Wang, Wenrui Liu, Yuhan Wu, Tong Yang, Lin Sun, and Xiangzheng Zhang. 2026. ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60506–60523, Budapest, Hungary. Association for Computational Linguistics.