# Incremental Hybrid-State Maintenance

This work implements a training-free incremental memory-maintenance runtime for a quantized Qwen hybrid recurrent–attention model that removes superseded attention KV entries, computes replacement records at the true sequence tail, and preserves the continuing recurrent state and unchanged KV; experiments show that independent block composition weakens query-conditioned memory selection, that true-tail updates preserve key current-state and historical bindings across eight scripted maintenance rounds, that slot-preserving alternatives repeat a double-subtraction error, and that attention-distribution proximity alone does not explain these semantic differences.

This work implements a training-free incremental memory-maintenance runtime for a quantized Qwen hybrid recurrent–attention model that removes superseded attention KV entries, computes replacement records at the true sequence tail, and preserves the continuing recurrent state and unchanged KV; experiments show that independent block composition weakens query-conditioned memory selection, that true-tail updates preserve key current-state and historical bindings across eight scripted maintenance rounds, that slot-preserving alternatives repeat a double-subtraction error, and that attention-distribution proximity alone does not explain these semantic differences.