递归语言模型：Claude Code与智能体群研究
Recursive Language Models: Claude Code, Agent Swarms, Big Research Bets, & the $40M AI Problem Solve...
MIT教授揭秘递归语言模型原理，对比Claude Code等模型，解释智能体群如何解决复杂问题。
MIT研究员@alanzhang解释Claude Code、Codex和Pi模型的相似性。递归语言模型通过代码、上下文卸载和递归子智能体实现跨任务泛化。OpenAI的10,000智能体、130B输出令牌实验揭示了语言模型未来发展方向。学术界最大优势是能进行大胆创新研究。
Recursive Language Models: Claude Code, Agent Swarms, Big Research Bets, & the $40M AI Problem Solve...
Recursive Language Models: Claude Code, Agent Swarms, Big Research Bets, & the $40M AI Problem Solver latent.space/p/rlm MIT’s @a1zhang explains why Claude Code, Codex, and Pi are basically the same, how RLMs use code, context offloading, and recursive subagents to generalize across tasks, why one expert can sometimes replace a trillion-token brute-force search, what OpenAI’s 10,000-agent, 130B-output-token experiment reveals about the future of language models, and why academia’s biggest advantage is the freedom to take weird, ambitious research bets. Your browser does not support the video tag. 🔗 View on Twitter 💬 0 🔄 0 ❤️ 5 👀 383 📊 1 ⚡
