ForkPilot: Self-Evolving Policy for Retrospective Search in Long-Horizon Agents

Excerpt

arXiv:2610.04889v1 Announce Type: new Abstract: Interactive language-model agents increasingly solve complex tasks through long-horizon, multi-call reasoning, where errors in beliefs or actions can compound across tool interactions. Retrospective search can recover from such failures but is prone to misallocation. Delayed outcomes obscure the contribution of intermediate search decisions, leading to Attribution Complexity, while evolving execution evidence leads to Adaptation Complexity, where p