Formalizing Agent Memory: Basis, Span, and Optimality Defined.
Key takeaways
- Agent memory is formally defined as a basis, with knowledge as its span.
- Optimal memory maximizes expected coverage within capacity constraints.
- The framework addresses noise, distinguishing coverage from precision.
- It provides a rigorous foundation for designing and evaluating agent memory systems.
Who benefits
Summary
This paper proposes a formal definition of agent memory, conceptualizing it as a basis whose span represents knowledge and answerability as a coverage problem. It introduces the optimal memory as a capacity-constrained maximizer of expected coverage and formalizes the continual agent-memory problem.
Why it matters
AI researchers and engineers can use this formal framework to design, evaluate, and optimize memory systems for large-model agents more rigorously, leading to more intelligent, efficient, and reliable AI systems.
How to implement this in your domain
- 1Apply the proposed formal definitions of memory basis and span to analyze existing agent memory architectures.
- 2Utilize the utility-capacity frontier concept to evaluate and compare the efficiency of different memory systems.
- 3Develop new memory write policies that explicitly address the trade-off between coverage and precision under noisy conditions.
- 4Frame the development of continual learning agent memory as a sequential MDP problem to guide optimization efforts.
Original post by Hongyao Tang
"arXiv:2608.11654v1 Announce Type: new Abstract: Despite the wide deployment of memory in large-model agents, there is no unified formal account of what a memory is or when it is optimal. This paper takes a first step toward this account. The central idea is that memory is a basis…"
View on XOriginally posted by Hongyao Tang on X · view source
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