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Beyond Heuristics: A Decision-Theoretic Framework for Agent Memory Management

Changzhi Sun, Xiangyu Chen, Jixiang Luo, Dell Zhang, Xuelong Li

TL;DR

The paper reframes memory management for long-lived LLM agents as a sequential decision problem under uncertainty, introducing the DAM framework that separates immediate read decisions from long-horizon write maintenance. Each write option is evaluated via a value function and an uncertainty estimate, and an Aggregate Policy arbitrates among read and write proposals to maximize long-term utility while mitigating risk. This principled view clarifies limitations of hand-designed heuristics and provides a structured path toward uncertainty-aware memory systems, with explicit directions for extending state representations, evaluating long-horizon effects, and designing diagnostic benchmarks. The work serves as a unifying theoretical blueprint for future research at the intersection of memory systems, RL, and sequential decision-making in AI agents.

Abstract

External memory is a key component of modern large language model (LLM) systems, enabling long-term interaction and personalization. Despite its importance, memory management is still largely driven by hand-designed heuristics, offering little insight into the long-term and uncertain consequences of memory decisions. In practice, choices about what to read or write shape future retrieval and downstream behavior in ways that are difficult to anticipate. We argue that memory management should be viewed as a sequential decision-making problem under uncertainty, where the utility of memory is delayed and dependent on future interactions. To this end, we propose DAM (Decision-theoretic Agent Memory), a decision-theoretic framework that decomposes memory management into immediate information access and hierarchical storage maintenance. Within this architecture, candidate operations are evaluated via value functions and uncertainty estimators, enabling an aggregate policy to arbitrate decisions based on estimated long-term utility and risk. Our contribution is not a new algorithm, but a principled reframing that clarifies the limitations of heuristic approaches and provides a foundation for future research on uncertainty-aware memory systems.

Beyond Heuristics: A Decision-Theoretic Framework for Agent Memory Management

TL;DR

The paper reframes memory management for long-lived LLM agents as a sequential decision problem under uncertainty, introducing the DAM framework that separates immediate read decisions from long-horizon write maintenance. Each write option is evaluated via a value function and an uncertainty estimate, and an Aggregate Policy arbitrates among read and write proposals to maximize long-term utility while mitigating risk. This principled view clarifies limitations of hand-designed heuristics and provides a structured path toward uncertainty-aware memory systems, with explicit directions for extending state representations, evaluating long-horizon effects, and designing diagnostic benchmarks. The work serves as a unifying theoretical blueprint for future research at the intersection of memory systems, RL, and sequential decision-making in AI agents.

Abstract

External memory is a key component of modern large language model (LLM) systems, enabling long-term interaction and personalization. Despite its importance, memory management is still largely driven by hand-designed heuristics, offering little insight into the long-term and uncertain consequences of memory decisions. In practice, choices about what to read or write shape future retrieval and downstream behavior in ways that are difficult to anticipate. We argue that memory management should be viewed as a sequential decision-making problem under uncertainty, where the utility of memory is delayed and dependent on future interactions. To this end, we propose DAM (Decision-theoretic Agent Memory), a decision-theoretic framework that decomposes memory management into immediate information access and hierarchical storage maintenance. Within this architecture, candidate operations are evaluated via value functions and uncertainty estimators, enabling an aggregate policy to arbitrate decisions based on estimated long-term utility and risk. Our contribution is not a new algorithm, but a principled reframing that clarifies the limitations of heuristic approaches and provides a foundation for future research on uncertainty-aware memory systems.
Paper Structure (36 sections, 12 equations, 1 figure)

This paper contains 36 sections, 12 equations, 1 figure.

Table of Contents

  1. Introduction
  2. A Decision-Theoretic Framework for Agent Memory Management
  3. Narrative
  4. Basic Model
  5. State Variables
  6. Decision Variables
  7. Transition Function
  8. Objective Function
  9. Modeling Uncertainty
  10. Designing Policies
  11. Read Policy
  12. Write Policy
  13. Sub-policy.
  14. Value Estimation.
  15. Uncertainty Quantification.
  16. ...and 21 more sections

Figures (1)

  • Figure 1: Overview of the hierarchical memory management policy. The read policy produces a retrieval action conditioned on the current state. Two write sub-policies (add, delete) independently propose candidate memory operations, each paired with a value estimate and an uncertainty score. The aggregate policy then arbitrates among these proposals to produce a coherent write action.