Principles that Guide, Actions that Inform: Agent Evolution via Knowledge Abstraction

Excerpt

arXiv:2610.06964v1 Announce Type: cross Abstract: Large language model (LLM) agents have demonstrated strong capabilities in interactive environments, yet their ability to continually evolve from experience remains limited. Although fine-tuning enables adaptation, its dependence on parameter access and high computational costs restrict its flexibility, especially for large-scale and closed-source LLMs. External memory offers an alternative by allowing agents to accumulate experience without modi