MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agents
Long-lived AI agents require continuity across interactions, but continuity cannot be obtained by simply extending the prompt window. An agent must preserve useful prior experience, retrieve it selectively, distinguish personal context from external evidence, and revise memory when the underlying situation changes. We propose an architectural memory substrate organized along two orthogonal axes: a representational axis spanning structured records, vector representations, and graph relations; and
Record details
Published: 6 July 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (6 July 2026), “MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agents,” evidence record 213, https://ethics.ai/record/213 (originally published by arXiv).
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