Did You Check the Right Pocket? Cost-Sensitive Store Routing for Memory-Augmented Agents
arXiv:2603.15658v1 Announce Type: new Abstract: Memory-augmented agents maintain multiple specialized stores, yet most systems retrieve from all stores for every query, increasing cost and introducing irrelevant context. We formulate memory retrieval as a store-routing problem and evaluate it using coverage, exact match, and token efficiency metrics. On downstream question answering, an oracle router achieves higher accuracy while using substantially fewer context tokens compared to uniform retrieval, demonstrating that selective retrieval improves both efficiency and performance. Our results show that routing decisions are a first-class component of memory-augmented agent design and motivate learned routing mechanisms for scalable multi-store systems. We additionally formalize store selection as a cost-sensitive decision problem that trades answer accuracy against retrieval cost, providing a principled interpretation of routing policies.
arXiv:2603.15658v1 Announce Type: new Abstract: Memory-augmented agents maintain multiple specialized stores, yet most systems retrieve from all stores for every query, increasing cost and introducing irrelevant context. We formulate memory retrieval as a store-routing problem and evaluate it using coverage, exact match, and token efficiency metrics. On downstream question answering, an oracle router achieves higher accuracy while using substantially fewer context tokens compared to uniform retrieval, demonstrating that selective retrieval improves both efficiency and performance. Our results show that routing decisions are a first-class component of memory-augmented agent design and motivate learned routing mechanisms for scalable multi-store systems. We additionally formalize store selection as a cost-sensitive decision problem that trades answer accuracy against retrieval cost, providing a principled interpretation of routing policies.
Executive Summary
This article introduces a novel approach to memory-augmented agents by formulating memory retrieval as a store-routing problem. The authors demonstrate that selective retrieval improves both efficiency and performance in downstream question answering tasks. They evaluate their approach using coverage, exact match, and token efficiency metrics, and provide a principled interpretation of routing policies as a cost-sensitive decision problem. The results show that learned routing mechanisms can achieve higher accuracy while using fewer context tokens, making them a promising area for scalable multi-store systems. The study's findings have significant implications for the design of efficient and accurate memory-augmented agents, which are essential for various applications, including natural language processing and information retrieval. The research provides valuable insights into the importance of routing decisions in memory-augmented agent design and highlights the potential of learned routing mechanisms to achieve improved performance and efficiency.
Key Points
- ▸ Memory retrieval is formulated as a store-routing problem to improve efficiency and performance in memory-augmented agents.
- ▸ Selective retrieval using learned routing mechanisms achieves higher accuracy and reduces context tokens.
- ▸ The study evaluates the approach using various metrics, including coverage, exact match, and token efficiency.
Merits
Strength in Formulation
The authors' formulation of memory retrieval as a store-routing problem is a significant contribution, as it highlights the importance of routing decisions in memory-augmented agent design.
Practical Significance
The study's findings have practical implications for the design of efficient and accurate memory-augmented agents, which are essential for various applications, including natural language processing and information retrieval.
Demerits
Limitation in Generalizability
The study's results may not be generalizable to all memory-augmented agent architectures, and further research is needed to explore the applicability of the proposed approach to different systems.
Lack of Theoretical Analysis
The study primarily focuses on empirical evaluations, and a more thorough theoretical analysis of the store-routing problem and its implications would provide a more comprehensive understanding of the approach.
Expert Commentary
The study's findings on learned routing mechanisms and selective retrieval are a significant contribution to the field of memory-augmented agents. The research highlights the importance of routing decisions in memory-augmented agent design and provides insights into the design of efficient and accurate memory-augmented agents. However, the study's limitations, including the lack of theoretical analysis and generalizability concerns, need to be addressed in future research. The findings have significant implications for the development of efficient and accurate memory-augmented agents, which are essential for various applications, including natural language processing and information retrieval.
Recommendations
- ✓ Further research is needed to explore the applicability of the proposed approach to different memory-augmented agent architectures.
- ✓ A more thorough theoretical analysis of the store-routing problem and its implications is necessary to provide a more comprehensive understanding of the approach.