ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions

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{this https URL}{demo}.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2608.30517 [cs.AI]

(or arXiv:2608.30517v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.30517 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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