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

2025-12-25

AI Summary

Existing external memory management for large language models relies on hand-crafted heuristics, which struggle with delayed long-term utility and interaction uncertainty. This paper formalizes the problem as a sequential decision-making task under uncertainty and proposes DAM—a decoupled memory operation paradigm that separates immediate access from hierarchical storage maintenance. DAM employs a decision framework grounded in value-function estimation and uncertainty quantification to explicitly balance long-term utility against risk, ensuring both interpretability and scalability. By integrating decision-theoretic modeling, rigorous uncertainty quantification, and policy aggregation, the work establishes a normative foundation for memory management, exposes fundamental limitations of heuristic approaches, and provides a principled design paradigm for uncertainty-aware memory systems.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Multiagent Systems under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Develops a decision-theoretic framework for managing agent memory
• Addresses limitations of heuristic-based memory management in LLMs
• Evaluates memory operations using long-term utility and risk estimation

Innovation

• Framing memory management as sequential decision-making under uncertainty
• Decomposing memory into immediate access and hierarchical storage maintenance
• Evaluating operations via value functions and uncertainty estimators