Nemori: Self-Organizing Agent Memory Inspired by Cognitive Science

• URL: http://arxiv.org/abs/2508.03341v2
• Date: Thu, 07 Aug 2025 05:39:56 GMT
• Title: Nemori: Self-Organizing Agent Memory Inspired by Cognitive Science
• Authors: Jiayan Nan, Wenquan Ma, Wenlong Wu, Yize Chen,
• Abstract summary: We present Nemori, a novel self-organizing memory architecture inspired by human cognitive principles.<n>Nemori's core innovation is principled, top-down method for autonomously organizing the raw conversational stream into semantically coherent episodes.<n>Nemori significantly outperforms prior state-of-the-art systems, with its advantage being particularly pronounced in longer contexts.
• Score: 1.4688849984602808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities, yet their inability to maintain persistent memory in long contexts limits their effectiveness as autonomous agents in long-term interactions. While existing memory systems have made progress, their reliance on arbitrary granularity for defining the basic memory unit and passive, rule-based mechanisms for knowledge extraction limits their capacity for genuine learning and evolution. To address these foundational limitations, we present Nemori, a novel self-organizing memory architecture inspired by human cognitive principles. Nemori's core innovation is twofold: First, its Two-Step Alignment Principle, inspired by Event Segmentation Theory, provides a principled, top-down method for autonomously organizing the raw conversational stream into semantically coherent episodes, solving the critical issue of memory granularity. Second, its Predict-Calibrate Principle, inspired by the Free-energy Principle, enables the agent to proactively learn from prediction gaps, moving beyond pre-defined heuristics to achieve adaptive knowledge evolution. This offers a viable path toward handling the long-term, dynamic workflows of autonomous agents. Extensive experiments on the LoCoMo and LongMemEval benchmarks demonstrate that Nemori significantly outperforms prior state-of-the-art systems, with its advantage being particularly pronounced in longer contexts.

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