Memory-Managed Long-Context Attention: Bounded Editable Memory with a Hard Lifecycle and Calibrated Sparse Fallback
We study memory-managed long-context attention: explicit bounded memory with a learned query-independent writer, lifecycle control, query-aware reading, calibrated sparse fallback, and frozen-LLM generation from raw evidence. Track A is a controlled versioned-variable task where last-mention retrieval is wrong by construction. Its full lifecycle scores 1.000 on all three seeds versus a 0.333 lexical baseline, and generation reaches 300/300 at 146 prompt tokens, compared with 172/300 for full-context reading at 729 tokens. Track B uses held-out HotpotQA questions and train-derived, answer-excluded distractors at natural and 8.2k-word lengths. A learned two-hop selector with a bounded 32-passage cache and fallback beats dense retrieval by 5.5--16.6 F1 and reaches 102--116% of full-context F1 at 10% of the evidence words. These real-text gains come from the learned selector; the cache preserves quality at a 0--2.9 F1 cost, and static QA text does not exercise overwrite or protection. The original Llama budget gate failure and the forward-adjudicated Qwen follow-up are reported explicitly. All backbones are frozen; joint training, faithful architecture baselines, and systems measurements remain future work.