ReMemorize: Reward-Guided Adaptive Memory Management for Faithful Clinical Text Summarization

Large language models show strong potential for clinical summarization, yet factual inconsistency remains a major concern in long-context generation. Existing approaches largely rely on retrieval, verification, or post-hoc refinement, while leaving autoregressive memory dynamics fixed and allowing supporting evidence to be progressively overwritten during generation. We propose ReMemorize, a framework that introduces explicit control over information retention through a recurrent memory state with gated updates during decoding. The interface is trained using supervised memory-consistency objectives, group-relative reward-weighted candidate learning, and a clipped score-weighted auxiliary gate objective based on sequence-level signals for factual alignment, coherence, completeness, and hallucination suppression. Across two popular summarization benchmarks, ReMemorize improves factual consistency over strong baselines, with gains concentrated on faithfulness rather than surface-level lexical overlap. Our results suggest that explicit control over information retention can improve factual grounding in clinical summarization. Our code is publicly available at https://anonymous.4open.science/r/ReMemorize-467B.