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arXiv 2606.02484cs.AIcs.LG
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Iteris: 计算数学的智能体研究循环

Iteris: Agentic Research Loops for Computational Mathematics

School of Mathematical Sciences, Peking University(北京大学数学科学学院);Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory, Peking University(北京大学北京国际数学研究中心和新基石科学实验室);Center for Machine Learning Research, Peking University(北京大学机器学习研究中心);Center for Intelligent Computing, Great Bay Institute for Advanced Study, Great Bay University(大湾研究院先进研究所智能计算中心);Zhongguancun Academy(中关村学院)

Leheng Chen, Zihao Liu, Wanyi He, Bin Dong

AI总结:

提出Iteris智能体研究系统,通过数值实验、构造和证明草稿解决计算数学中的两个开放问题,经专家验证后获得可验证结果。

摘要:

大型语言模型和智能体AI系统的最新进展使得数学发现取得了显著进展,从解决竞赛问题到处理研究级猜想。然而,计算数学中的开放问题受到的关注相对较少:该领域的研究通常不仅需要证明,还需要数值实验、对抗性构造和算法设计。在本文中,我们介绍了一个面向计算数学开放问题的智能体研究系统Iteris。我们将Iteris应用于近期Simons Workshop论文集(arXiv:2602.05394)中的两个开放问题。在这些案例研究中,Iteris生成了数值证据、构造和证明草稿,经过专家评审和修正后,得到了可验证的结果。第一个结果是关于幂律谱上共轭梯度与随机坐标下降渐近比较的相图;第二个结果是一个反例,表明即使低相干性下,带列主元的QR分解也可能无法选择良态子矩阵。这些案例研究表明,智能体AI系统可以有意义地参与计算数学开放问题的研究工作流程,而人工验证仍然至关重要。

英文摘要:

Recent advances in large language models and agentic AI systems have enabled significant progress in mathematical discovery, from solving competition problems to tackling research-level conjectures. However, open problems in computational mathematics have received comparatively less attention: research in this area often requires not only proofs but also numerical experimentation, adversarial constructions, and algorithm design. In this paper, we introduce an agentic research system, Iteris, designed for open problems in computational mathematics. We apply Iteris to two open problems from a recent Simons Workshop collection (arXiv:2602.05394). In these case studies, Iteris generated numerical evidence, constructions, and proof drafts that led, after expert review and correction, to verified results. The first result is a phase diagram for the asymptotic comparison between conjugate gradient and randomized coordinate descent on power-law spectra; the second is a counterexample showing that QR factorization with column pivoting can fail to select well-conditioned submatrices even under low coherence. These case studies suggest that agentic AI systems can participate meaningfully in research workflows for open problems in computational mathematics, while human validation remains essential.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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