用后训练让大模型自动形式化运筹问题，规模化方案值得关注
arXiv:2604.16804v3 Announce Type: replace-cross Abstract: Optimization problems are central to decision-making in manufacturing, logistics, scheduling…
自博弈遇上自指导：新方法以自我引导驱动大规模训练，显著提升智能体策略多样性。
arXiv:2604.20209v2 Announce Type: replace Abstract: LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conje…
借鉴DNS分布式架构，实现百万级AI工具的高效语义发现，突破传统集中式管理的O(N)瓶颈，为自主智能体时代铺路。
arXiv:2607.18242v1 Announce Type: new Abstract: The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of…
用结构化思维链攻克十亿级低活跃用户建模难题，稀疏画像下也能精准推理。
arXiv:2606.24605v1 Announce Type: new Abstract: Accurate user modeling often depends on rich interaction histories, which are unavailable for billions…
让你的AI工作流再上一层楼——用AI来驱动AI，实现规模化智能应用。
Article URL: https://omniscientiq.com/ Comments URL: https://news.ycombinator.com/item?id=48484597 Points: 2 # Comments: 0