arXiv Computation and Language August 27, 2026 By Wujiang Xu, Jiaojiao Han, Minghao Guo, Kai Mei, Xi Zhu, Han Zhang, Dimitris N. Metaxas

AEL: Evolving Agent Harness in Open-Ended Environments

Read the original on arXiv Computation and Language →

The paper introduces Agent Evolving Learning (AEL), a two‑timescale framework that dynamically evolves an LLM agent’s memory‑retrieval harness in open‑ended environments. A fast Thompson‑Sampling bandit selects among retrieval policies each episode, while a slower LLM reflection diagnoses performance drops and injects new policies when the current set plateaus. AEL outperforms ten self‑improving and non‑LLM baselines on a sequential portfolio benchmark, boosting Sharpe ratio by 27% and achieving significant accuracy gains on a support‑ticket routing stream.

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