Large language models (LLMs) are largely static and often redo reasoning or repeat mistakes.Prior experience reuse typically relies on external retrieval, which is similarity-based, can introduce noise, and adds latency.We introduce SEAM (Structured Experience Adapter Module), a lightweight, executor-specific plugin that stores experience in its parameters and generates a structured, instance-tailored experience entry in a single forward pass to guide a frozen LLM executor.SEAM is trained for utility via executor rollouts and GRPO while keeping the executor frozen, and can be further improved with logged-success SFT after deployment.Experiments on mathematical reasoning benchmarks show consistent accuracy gains across executors with low overhead.Extensive ablation and analysis further elucidate the mechanisms underlying SEAM's effectiveness and robustness. 1 † Equal contribution.

Web search heavily relies on click-through behavior as an essential feedback signal for performance evaluation and improvement. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click is regarded as a signal of irrelevance or uselessness. However, there are many cases where users satisfy their information need with the contents shown on the Search Engine Result Page (SERP). This raises the problem of measuring the usefulness of non-click results and modeling user satisfaction in such circumstances.