首个面向递归自我改进的AI智能体算法设计基准，直击AI自我进化核心命题。
arXiv:2608.20318v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI …
把预测引入在线顶点覆盖，突破传统竞争比瓶颈，理论计算机新进展。
arXiv:2606.22831v1 Announce Type: cross Abstract: This paper studies learning-augmented online weighted vertex cover with advice and a parameter $\lam…
手把手教你从零构思学习算法，把抽象设计思路拆解成可操作的步骤，适合想深入算法原理的读者。
arXiv:2604.07328v3 Announce Type: replace Abstract: How does the choice of training data influence an AI model? This broad question is of central impo…
融合机器学习增强技术，突破传统精确指数算法的效率瓶颈。
arXiv:2606.18807v1 Announce Type: cross Abstract: The field of learning-augmented algorithms has demonstrated that machine-learned predictions can byp…
将持续学习与机器遗忘视为对偶问题，提出PURGE算法实现精准数据擦除，思路新颖且实用。
arXiv:2606.03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observatio…
探索LLM与进化搜索结合时，代码进化究竟在优化什么——对算法设计的关键追问
arXiv:2605.20086v1 Announce Type: cross Abstract: Recent work pairs LLMs with evolutionary search to iteratively generate, modify, and select code usi…