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PILOT in the Loop: Live Self-Improvement for Long-Horizon Agents

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

Long-horizon agent runs generate experience that can improve both the current run and future work. Most self-improvement methods process this experience only after execution ends, so they cannot redirect the active run or immediately apply and validate lessons learned from it. We argue that self-improvement should instead be live, using emerging experience both to redirect the active run and to update the persistent harness. Existing agent architectures do not fully support this goal. Single-agent self-correction combines task execution and trajectory assessment within one context, while subag

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