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RPMem: Learning Long-Term Recurrent Parametric Memory Across Sessions for LLM Agents

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

Long-running LLM agents require memory that persists and evolves across sessions. Text-based memory retrieves and reconstructs past interactions at every query, making long-horizon performance increasingly dependent on retrieval quality and contextual reasoning as histories grow. Parametric memory encodes experience directly into model computation, but existing approaches provide limited support for cross-session memory evolution. Their coupling to a specific backbone further restricts memory reuse after model replacement. We introduce RPMem, a two-stage architecture that compiles each session

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