UniMem

UniMem: Self-routing framework for complementary episodic-to-parametric memory in LLM agents

UniMem is a proposed framework for autonomous memory management in LLM agents that addresses the stability-plasticity dilemma in boundary-agnostic task streams. The system uses learnable routing tokens to coordinate between an episodic retrieval buffer (for novel/sparse tasks) and expandable parametric memory blocks (for recurring patterns), inspired by human memory consolidation. Experiments on long-horizon streaming task sequences show an average 4.0 EM point gain over baselines across three backbone models. The work is relevant to continual learning and agent memory architecture research.