Recursive Minds: How ReCAP Turns LLMs into Self-Correcting Planners
In long-horizon reasoning, large language models still behave like short-term thinkers. They can plan, but only in a straight line. Once the context window overflows, earlier intentions vanish, and the model forgets why it started. The new framework ReCAP (Recursive Context-Aware Reasoning and Planning)—from Stanford’s Computer Science Department and MIT Media Lab—offers a radical solution: give LLMs a recursive memory of their own reasoning. The Problem: Context Drift and Hierarchical Amnesia Sequential prompting—used in CoT, ReAct, and Reflexion—forces models to reason step by step along a linear chain. But in complex, multi-stage tasks (say, cooking or coding), early goals slide out of the window. Once the model’s focus shifts to later steps, earlier plans are irretrievable. Hierarchical prompting tries to fix this by spawning subtasks, but it often fragments information across layers—each sub-agent loses sight of the global goal. ...