From Case Feedback to Auditable Memory: Context Evolution for Complete Blood Count-Based Risk-Oriented Reasoning

Large language models (LLMs) show promise in medical reasoning, yet accumulating reusable and auditable experience from structured laboratory findings across case streams remains challenging. To address these issues, we propose a feedback-driven context evolution framework that adapts frozen LLMs through structured memory and inference time context updates. Specifically, we first construct a hierarchical memory architecture that separates stable knowledge from dynamic experience and organizes audit events, memory cards, and retrieval indices to preserve clinical guidance and traceable case-derived experience. We then design a deterministic write gate that checks consistency, merges compatible rules, isolates conflicts, and routes ambiguous candidates for human review, thereby controlling how new experience enters memory. At inference time, section-first retrieval dynamically adjusts the number and composition of retrieved cards to the configured context capacity, integrating relevant stable knowledge with case-derived experience. We evaluate the framework on complete blood count-based case streams for disease detection, tumor risk assessment, and candidate tumor group ranking. Compared with retrieval-augmented generation and other context optimization baselines, the framework improves disease detection and candidate tumor group ranking, while exhibiting sustained performance across a long-horizon case stream.