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  • LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in . . .
    Drawing on knowledge from a group of medalists in international algorithmic contests, we revisit this claim, examining how LLMs differ from human experts and where limitations still remain We introduce LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI that are continuously updated to reduce the likelihood of
  • It is clear that the state-of-the-art large-scale language . . .
    The coding capabilities of large-scale language models (LLMs) are so high that technology company leaders have said things like, ' In LiveCodeBench Pro, a team of International Olympiad medalists
  • How Do Olympiad Medalists Judge LLMs in Competitive . . .
    A new benchmark assembled by a team of International Olympiad medalists suggests the hype about large language models beating elite human coders is premature LiveCodeBench Pro, unveiled in a 584-problem study [PDF] drawn from Codeforces, ICPC and IOI contests, shows the best frontier model clears j
  • 2025년 6월 16일 - by Kim Seonghyeon - arXiv Daily
    Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming Drawing on knowledge from a group of medalists in international algorithmic contests, we revisit this claim, examining how LLMs differ from human experts and where limitations still remain
  • Can Language Models Solve Olympiad Programming? - arXiv. org
    Our benchmark, baseline methods, quantitative results, and qualitative analysis serve as an initial step toward LMs with grounded, creative, and algorithmic reasoning Code generation has become an important domain to evaluate and deploy language models (LMs)
  • LiveCodeBench Pro: How Do Olympiad MedalistsJudge LLMs in . . .
    This paper introduces LiveCodeBench Pro, a new benchmark designed to rigorously evaluate large language models (LLMs) in competitive programming using expert
  • Examining Knowledge in Large Language Models - Simple Science
    In this paper, we analyze the internal knowledge structures of LLMs using historical medal tallies from the Olympic Games We task the models with providing the medal counts for each team and identifying which teams achieved specific rankings





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