{"url":"/dataset/olympicarena","name":"OlympicArena","full_name":null,"description_markdown":"OlympicArena is a benchmark to evaluate advanced capabilities of language models across a broad spectrum of Olympic-level challenges. \r\n\r\n# Comprehensive: \r\nThe benchmark includes a comprehensive set of 11,163 problems from 62 distinct Olympic competitions, structured with 13 answer types. It spans seven core disciplines: mathematics, physics, chemistry, biology, geography, astronomy, and computer science, encompassing a total of 34 specialized branches\r\n# High-challenging: \r\nThe benchmark focuses on Olympic-level problems and covers 8 types of logical reasoning abilities and 5 types of visual reasoning abilities.\r\n# Rigorous: \r\nGiven the increasing scale of pre-training corpora, it is crucial to detect potential benchmark leakage. We employ a recently proposed instance-level leakage detection metric to validate our benchmark’s effectiveness.\r\n# Fine-grained Evaluation: \r\nWe conduct comprehensive evaluations from both the answer-level and process-level perspectives. Additionally, we perform fine-grained evaluations and analyses on different types of cognitive reasoning, from both logical and visual perspectives to better interpret the current capabilities of AI.","description_withheld":null,"homepage":"https://github.com/GAIR-NLP/OlympicArena","introduced_date":"2024-06-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/olympicarena-benchmarking-multi-discipline","title":"OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI","first_author":"Zhen Huang","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["OlympicArena"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}