Language-model-based agents operating over extended interaction horizons face persistent challenges in preserving temporally grounded information and maintaining behavioral consistency across sessions, a failure mode we term soul erosion. We present BMAM (Brain-inspired Multi-Agent Memory), a general-purpose memory architecture that models agent memory as a set of functionally specialized subsystems rather than a single unstructured store. Inspired by cognitive memory systems, BMAM decomposes memory into episodic, semantic, salience-aware, and control-oriented components that operate at complementary time scales. To support long-horizon reasoning, BMAM organizes episodic memories along explicit timelines and retrieves evidence by fusing multiple complementary signals. Experiments on the LoCoMo benchmark show that BMAM achieves 78.45 percent accuracy under the standard long-horizon evaluation setting, and ablation analyses confirm that the hippocampus-inspired episodic memory subsystem plays a critical role in temporal reasoning.

翻译：基于语言模型的智能体在长时间交互中面临持续挑战：难以保持时间锚定信息，且在多个会话间维持行为一致性——我们将这种失效模式称为灵魂侵蚀。本文提出BMAM（受大脑启发的多智能体记忆），一种通用记忆架构，它将智能体记忆建模为一组功能特化的子系统，而非单一非结构化存储。受认知记忆系统启发，BMAM将记忆分解为情景记忆、语义记忆、显著性感知记忆与控制导向记忆四个在互补时间尺度上运作的组件。为支持长程推理，BMAM沿显式时间线组织情景记忆，并通过融合多重互补信号进行证据检索。在LoCoMo基准测试中，BMAM在标准长程评估设定下达到78.45%的准确率，消融实验证实受海马体启发的的情景记忆子系统在时序推理中起关键作用。