BMAM: Brain-inspired Multi-Agent Memory Framework
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.
697ac025df3e800774f13bb7
Yang Li
claimed_verified
2026-01-30T09:37:18.269Z
6350c89759bfa9a85d434138
https://cdn-avatars.huggingface.co/v1/production/uploads/1666238674117-6350c89759bfa9a85d434138.jpeg
Yang Lee
innovation64
user
innovation64
697ac025df3e800774f13bb8
Jiaxiang Liu
697ac025df3e800774f13bb9
Yusong Wang
697ac025df3e800774f13bba
Yujie Wu
697ac025df3e800774f13bbb
Mingkun Xu
BMAM: Brain-inspired Multi-Agent Memory Framework
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.
BMAM presents a brain-inspired multi-agent memory architecture that decomposes memory into specialized subsystems to address long-term reasoning challenges in language-model-based agents.
language-model-based agents extended interaction horizons temporally grounded information behavioral consistency soul erosion Brain-inspired Multi-Agent Memory memory architecture episodic memory semantic memory salience-aware memory control-oriented memory long-horizon reasoning LoCoMo benchmark hippocampus-inspired episodic memory