How should experience act? Adaptive experience binding for agent self-evolution

Large language model (LLM) agents are increasingly used to handle complex real-world tasks. To reduce the effort of manually designing and optimizing agents, recent work explored $\text{agent self-evolution}$, enabling agents to improve themselves toward target tasks automatically. Existing approaches either iteratively search for optimal agent configurations or accumulate and reuse experience to guide subsequent behavior. While the former often requires repeated rollout-and-evaluation cycles, the latter provides a more direct mechanism for preserving and exploiting execution experience. However, existing methods focus on what experiences to learn and retrieve, but leave a critical question largely under-explored: $\text{how should learned experience act during execution?}$ Our measurement studies show that different ways of applying the same experience can substantially influence both agent behavior and end-to-end task performance. To address this gap, we propose $\text{Adaptive Experience Binding (AEB)}$, a training-free framework that explicitly regulates when and how learned experience influences agent behavior. AEB represents experience as structured and revisable Binding Experiences, learns and updates them from grounded execution evidence, and dynamically determines the appropriate form and strength of their influence according to the current execution state. Extensive experiments on complex agentic benchmarks demonstrate consistent improvements over representative baselines, with additional analysis experiments further supporting the effectiveness of the proposed design.