MIT: Recursive Language Models: Prompts as REPL скачать в хорошем качестве

MIT: Recursive Language Models: Prompts as REPL

🧠 Unlock the next level of AI reasoning! https://www.emergent-behaviors.com/an... In this video, we dive into the groundbreaking paper "Recursive Language Models" by researchers from MIT CSAIL. Discover how giving LLMs a "Library Card" allows them to navigate massive datasets and solve complex, multi-step problems that leave standard models overwhelmed. We explore the concept of "Context Rot," how RLMs use a Python REPL environment to "peek" into prompts, and why this approach is not only more powerful but also surprisingly cost-effective. 📉✨ 📌 What You’ll Learn: Why longer prompts often lead to worse AI performance (Context Rot). How the RLM framework acts like an "Out-of-Core" algorithm for text. The power of recursive sub-calling to untangle messy, information-dense tasks. Real-world performance benchmarks comparing GPT-5 with and without RLM. ⏳ Timestamps: 0:00 Introduction to Recursive Language Models (RLMs) 0:41 The Problem: "Context Rot" in Modern LLMs 1:25 Performance Cliff: Why context length ≠ usability 2:14 The RLM Idea: Treat prompts as an environment 2:57 How it works: REPL environments & Task Delegation 3:41 Results: Maintaining performance at 1M+ tokens 4:27 Cost Analysis: Is RLM more expensive? 5:07 Solving Length vs. Complexity 5:56 Final Takeaway: Why tools make LLMs more powerful 6:38 Read the full paper (Link) RECURSIVE LANGUAGE MODELS https://arxiv.org/pdf/2512.24601v1 Alex L. Zhang, MIT CSAIL, altzhang@mit.edu Tim Kraska, MIT CSAIL, kraska@mit.edu Omar Khattab, MIT CSAIL, okhattab@mit.edu #AI #MachineLearning #MIT #RLM #RecursiveLanguageModels #LLM #FutureOfTech #ComputerScience #AIResearch #ContextRot #TechNews #GenerativeAI #MITCSAIL