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Continuum Memory Architectures for Long-Horizon LLM Agents

Joe Logan

TL;DR

The paper identifies a gap in long‑horizon LLM agents: memory should evolve over time rather than remain as static retrieval data. It defines Continuum Memory Architectures (CMA) as a persistent, mutable memory substrate with selective retention, associative routing, temporal continuity, and consolidation, operating via an ingest–activation–retrieval–consolidation lifecycle. Through four behavioral probes, CMA demonstrates clear advantages over a strong RAG baseline in knowledge updates, temporal association, associative recall, and disambiguation, while highlighting latency, drift, and interpretability as key challenges. The work positions CMA as a foundational architectural primitive for reliable, persistent AI agents and provides a concrete instantiation and evaluation framework to catalyze further research and safe deployments.

Abstract

Retrieval-augmented generation (RAG) has become the default strategy for providing large language model (LLM) agents with contextual knowledge. Yet RAG treats memory as a stateless lookup table: information persists indefinitely, retrieval is read-only, and temporal continuity is absent. We define the \textit{Continuum Memory Architecture} (CMA), a class of systems that maintain and update internal state across interactions through persistent storage, selective retention, associative routing, temporal chaining, and consolidation into higher-order abstractions. Rather than disclosing implementation specifics, we specify the architectural requirements CMA imposes and show consistent behavioral advantages on tasks that expose RAG's structural inability to accumulate, mutate, or disambiguate memory. The empirical probes (knowledge updates, temporal association, associative recall, contextual disambiguation) demonstrate that CMA is a necessary architectural primitive for long-horizon agents while highlighting open challenges around latency, drift, and interpretability.

Continuum Memory Architectures for Long-Horizon LLM Agents

TL;DR

The paper identifies a gap in long‑horizon LLM agents: memory should evolve over time rather than remain as static retrieval data. It defines Continuum Memory Architectures (CMA) as a persistent, mutable memory substrate with selective retention, associative routing, temporal continuity, and consolidation, operating via an ingest–activation–retrieval–consolidation lifecycle. Through four behavioral probes, CMA demonstrates clear advantages over a strong RAG baseline in knowledge updates, temporal association, associative recall, and disambiguation, while highlighting latency, drift, and interpretability as key challenges. The work positions CMA as a foundational architectural primitive for reliable, persistent AI agents and provides a concrete instantiation and evaluation framework to catalyze further research and safe deployments.

Abstract

Retrieval-augmented generation (RAG) has become the default strategy for providing large language model (LLM) agents with contextual knowledge. Yet RAG treats memory as a stateless lookup table: information persists indefinitely, retrieval is read-only, and temporal continuity is absent. We define the \textit{Continuum Memory Architecture} (CMA), a class of systems that maintain and update internal state across interactions through persistent storage, selective retention, associative routing, temporal chaining, and consolidation into higher-order abstractions. Rather than disclosing implementation specifics, we specify the architectural requirements CMA imposes and show consistent behavioral advantages on tasks that expose RAG's structural inability to accumulate, mutate, or disambiguate memory. The empirical probes (knowledge updates, temporal association, associative recall, contextual disambiguation) demonstrate that CMA is a necessary architectural primitive for long-horizon agents while highlighting open challenges around latency, drift, and interpretability.
Paper Structure (16 sections, 1 figure, 1 table)

This paper contains 16 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Definition: Continuum Memory Requirements
  4. Continuum Memory Architecture Overview
  5. Behavioral Evaluation
  6. Evaluation Protocol.
  7. Statistical Testing.
  8. Selective Retention: Knowledge Updates
  9. Temporal Continuity: Temporal Association
  10. Associative Routing: Associative Recall
  11. Contextual Partitioning: Disambiguation
  12. Summary and Costs
  13. Failure Modes and Limitations
  14. Discussion
  15. Conclusion
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Conceptual lifecycle of a Continuum Memory Architecture illustrating ingest, activation, retrieval, and consolidation stages. Details are abstracted for clarity.