arXiv:2604.23878v4 Announce Type: replace

Abstract: ZenBrain is a seven-layer, neuroscience-derived architecture for agent memory in LLM agents. An open library holds the seven layers, a MemoryCoordinator and nine foundational algorithms; six Predictive Memory Architecture components run in a private production system. In the open library at version 0.5.1, decay acts only on working memory and nothing is replayed.

Ablating one component at a time exposes an effect we call cooperative masking. The ablation runs in a simulation harness that switches fifteen components as stand-ins at simulated values, not as production code. Under moderate load, fourteen of the fifteen ablations look costless. Raising decay, the horizon, the corpus and the queries, with no change to the components, moves ten of the fifteen in the same direction on all ten seeds - nine losses (Delta-Q down to -93.7%) and one gain. With ten seeds this is the one p-value the normal approximation of the Wilcoxon test can reach (5.06e-3), and it does not pass the Bonferroni-corrected level for fifteen contrasts (3.33e-3).

On LongMemEval_S (500 questions), the evaluated ZenBrain arm - a Python port of a scoring class from our test harness, not the seven-layer system - answers 47.7% of the questions correctly under a binary judge (95% interval [43.3, 52.1]). With the compared systems' adapters repaired, Letta reaches 48.4% and Mem0 47.2%. ZenBrain reaches 91.3% of a full-context baseline's accuracy with 1/109.7 of its input tokens in the answer step; building the memory is not counted. Of nine head-to-head comparisons of the retrieved context (3 competitors x 3 LLM judges), three hold at Bonferroni-corrected significance, all against A-Mem, and the remaining six show no significant difference.

On LoCoMo, the routing results of version 8 came from placeholder embeddings; with text-embedding-3-small, the flat variant (F1 0.059) lies above the full system (0.058).