MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agents

2026-07-05

AI Summary

This work addresses the lack of cross-interaction memory continuity in existing long-lived AI agents, which struggle to effectively preserve, selectively retrieve, and dynamically update personal experiences. The authors propose a multi-resolution memory substrate organized along two axes: representation (structured records, vector embeddings, and graph-based relations) and time (short-term traces, mid-term abstractions, and long-term semantic commitments). A synchronized structure-vector-graph mechanism enables selective memory retrieval, validation, and integration. Innovatively framing personalized reliability as a memory design problem, the approach emphasizes structured storage, selective exposure, continual integration, and cognitive tagging. A prototype system demonstrates the framework’s capability in pre-generating memory candidates, revising content, enforcing boundary constraints, and tracing evidential provenance, validating its efficacy in extended interaction scenarios.

Abstract

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 a temporal axis spanning short-term traces, medium-term abstractions, and long-term semantic commitments. Its key design constraint is synchronized structured-vector-graph memory: structured records govern eligibility, vector representations support recall, and graph relations adjudicate support, contradiction, and supersession before gated context projection. Its central claim is that reliable personalization is a memory design problem: useful memory is structured, selectively exposed, continuously consolidated, and epistemically labeled rather than stored as undifferentiated conversation history. Beyond the framework, we instantiate MRMS as a lightweight prototype implementing structured records, vector retrieval, temporal policies, and graph-based revision. The prototype exercises the core substrate mechanisms through pre-generation memory selection, revision, boundary enforcement, and evidence attribution under controlled long-lived interaction scenarios with explicit evidence requirements.

Problem

• long-lived AI agents
• memory continuity
• personal context
• memory revision
• experience preservation

Innovation

• multi-resolution memory
• structured-vector-graph memory
• long-lived AI agents
• epistemic memory labeling
• temporal memory abstraction