AI 研究员自己跑通宵，数学纪录被刷了120次
一个叫 Épi 的 AI 研究员，能跨会话持续研究、自己积累经验、自己改方法，还自己跑实验。它刷出了 120 项纪录：把 n 皇后常数的不确定区间缩窄了 84 倍，把四次 Feigenbaum 吸引子维度的严格区间缩窄了 360 万倍，还顺带证明了图论里 Tuza 猜想的一个特殊情形。更实际的是，它的轻量版 Épi-turbo 在 30 个运筹学任务上，用同一模型、64% 的成本，总分比 OpenAI 的 Codex 高 5.4%。这不是你明天能用的工具，但它指向一个真实趋势：AI 从「答一道题」走向「自己立项、自己验证、自己写论文」，数学和算法研究里那些靠人熬夜堆的苦活，正在被自动化。
📄 原文摘要(英文)
We present Épi, an autonomous research agent that sustains investigations across model sessions and evolves its methods through accumulated experience. Épi combines persistent research history, hierarchical memory, branching and independent review to maintain research continuity, learn from experiments, and explore and revisit alternative approaches. Its experiments run across session boundaries, and a fixed verifier evaluates their outputs. Épi produced 120 record-setting results, comprising 20 numerical bound records across 12 families and 100 construction records across eight families. These include an approximately 84-fold narrower interval for the n-queens constant and a more than 3.6-million-fold narrower rigorous interval for the quartic Feigenbaum attractor dimension. Exact dual certificates establish that the Cohn-Elkies linear-programming bound cannot be sharp in dimensions 10 and 11. Beyond numerical improvements, Épi gives a computer-assisted proof of Tuza's conjecture for split graphs with an eight-vertex clique part and at most three triangle-active neighborhood types, with arbitrary multiplicities. We extend this research process to algorithmic coding with Épi-turbo, a lightweight configuration that develops executable solvers for tasks including vehicle routing, scheduling and production planning. On 30 FrontierOR tasks, Épi-turbo achieves a 5.4% higher total benchmark score than Codex at 64% of its model cost, using the same underlying model and reasoning effort. Épi is designed to support human understanding by connecting computational results to explicit methods and mathematical arguments that researchers can examine and build upon.