AI Agents

In December 2025, Alex Zhang and collaborators at MIT published a paper called Recursive Language Models. The core result: GPT-5 scores 0% on a retrieval task over 1000 documents (10M+ tokens). RLM wrapping the same GPT-5 scores 91.3%. Same model. The difference is how context is managed. That result stuck with me. I’ve been building recursive agent systems on top of it since. This post is about what RLM actually is, why it works, and what I’ve learned running it on real tasks. ...

The problem: MCP agents return bulky tool outputs (screenshots, DOM dumps, network traces) and quickly blow past context limits. Downstream steps stall or get fuzzy because the signal is buried. TL;DR: MCP Compact sits between your agent and MCP server, summarizes noisy tool outputs per-tool, and keeps context lean (e.g., 109k DOM -> 8.9k tokens) without changing agent code. What MCP Compact does: it sits between your agent and the upstream MCP server, forwards every tool call, and summarizes the response with an LLM. You set per-tool rules (token budget, what to preserve), and the proxy enforces them automatically. ...