PILOT in the Loop: Live Self-Improvement for Long-Horizon Agents
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 subagent delegation separates execution but typically cannot redirect an active subagent. We present P...
| Authors | Yang Xiao, Yusong Sun, Haoyi Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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