An agent branches off to handle a sub-task, then folds that work back into a short summary — collapsing intermediate steps so its working context stays small.
What it is
Long-horizon agents drown in their own history: every tool call, observation, and dead end piles into the context window until recall degrades. Context folding attacks this directly. When an agent spins up a sub-task, it works in a branch, and on completion it discards the raw intermediate steps and folds only a compact summary back into the main thread.
The result is an agent that can run for hundreds of steps while keeping its live context an order of magnitude smaller than a flat ReAct loop — more signal, less rot.
Why it's worth watching
Introduced in “Scaling Long-Horizon LLM Agent via Context-Folding” (ByteDance Seed, CMU, Stanford; arXiv:2510.11967, Oct 2025).
Reported 62.0% on BrowseComp-Plus and 58.0% on SWE-Bench Verified on a 32K token budget vs. ReAct baselines needing ~327K context.
Multiple independent follow-ups within months (AgentFold, ACON, and others) — a term crossing from one lab to a research cluster.
The composition angle
Context folding is composition in the time dimension: instead of one giant context, you compose a chain of small, summarized ones. It is the clearest example of why the future is many tightly-scoped contexts, not one infinite window.