데브허브 | DEVHUB | The MIT Paper Everyone Building Agents Should Read Right Now
🔗 AI Engineering Consultancy: https://brainqub3.com
🔗 Brainqub3 Check (AI fact-checker): https://check.brainqub3.com
Recursive Language Models (RLMs) - MIT's approach to extending effective context windows by 100x
This paper from MIT researchers (Alex Zhang, Tim Kraska, Omar Khattab) presents a surprisingly practical solution to context rot in LLMs. Instead of cramming everything into one massive context window, RLMs treat the prompt as an external variable in a Python REPL and let the model recursively call itself over smaller chunks.
The results are significant: handling 10M+ tokens effectively, outperforming base models by double-digit percentages on complex tasks, and doing it at comparable (sometimes cheaper) cost per query.
What makes this particularly interesting is that you can implement this today with existing models and infrastructure. No fine-tuning required.
In this video:
What RLMs actually are and how they work
The context rot problem they solve
Benchmark results across code understanding, document QA, and semantic aggregation
Why this matters for production AI agents
Practical implementation considerations
Links:
📄 Paper: https://arxiv.org/abs/2512.24601
💻 GitHub: https://github.com/alexzhang13/rlm
📝 Alex Zhang's blog post: https://alexzhang13.github.io/blog/20...
#AIAgents #LLM #ContextWindow #MIT #MachineLearning #AppliedAI
![[OpenAI Whisper CS-based File ASR]_07. ASR 처리를 위한 클라이언트 - 서버 메시지 교환 로직의 이해](https://i3.ytimg.com/vi/6ZMVdTqzSbA/hqdefault.jpg)
![[우분투 리눅스 기초 강의] 3강. VMware 특징, VMnet8 설정](https://i3.ytimg.com/vi/rpxSkWvloLc/hqdefault.jpg)