English(EN) InteractBench: Benchmarking LLMs on Competitive Programming under Unrevealed Information

新基准揭示大语言模型在交互式编程问题上存在困难

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员推出了 InteractBench，这是一个旨在评估大语言模型 (LLM) 在交互式问题上的算法推理能力的新基准。这些问题在竞技编程中很常见，要求模型通过查询动态获取信息，而不是预先提供所有输入。该基准包括来自 Codeforces 和 AtCoder 等平台的 322 个问题，以及用于离线评估的本地交互器。初步结果显示性能存在显著差距，目前先进的模型在交互式任务上表现不佳，除了算法逻辑错误外，还经常因协议违规或超出查询预算而失败。 AI

影响标志着大语言模型评估的新前沿，将评估从静态问题解决推向动态、交互式推理。

排序理由该集群包含一篇介绍大语言模型评估新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jiaze Li, Aocheng Shen, Bing Liu, Boyu Zhang, Xiaoxuan Fan, Qiankun Zhang, Xianjun Deng · 2026-09-02 04:00

InteractBench：在信息未公开的竞赛编程场景下对大型语言模型进行基准测试

arXiv:2608.29632v1 Announce Type: cross Abstract: Competitive programming is increasingly being used to evaluate the algorithmic reasoning capabilities of large language models (LLMs). However, existing benchmarks primarily focus on full-information tasks where all problem inputs…

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报道来源 [1]

InteractBench：在信息未公开的竞赛编程场景下对大型语言模型进行基准测试

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