[2603.02473] Diagnosing Retrieval vs. Utilization Bottlenecks in LLM Agent Memory

Computer Science > Artificial Intelligence arXiv:2603.02473 (cs) [Submitted on 2 Mar 2026] Title:Diagnosing Retrieval vs. Utilization Bottlenecks in LLM Agent Memory Authors:Boqin Yuan, Yue Su, Kun Yao View a PDF of the paper titled Diagnosing Retrieval vs. Utilization Bottlenecks in LLM Agent Memory, by Boqin Yuan and 2 other authors View PDF HTML (experimental) Abstract:Memory-augmented LLM agents store and retrieve information from prior interactions, yet the relative importance of how memories are written versus how they are retrieved remains unclear. We introduce a diagnostic framework that analyzes how performance differences manifest across write strategies, retrieval methods, and memory utilization behavior, and apply it to a 3x3 study crossing three write strategies (raw chunks, Mem0-style fact extraction, MemGPT-style summarization) with three retrieval methods (cosine, BM25, hybrid reranking). On LoCoMo, retrieval method is the dominant factor: average accuracy spans 20 points across retrieval methods (57.1% to 77.2%) but only 3-8 points across write strategies. Raw chunked storage, which requires zero LLM calls, matches or outperforms expensive lossy alternatives, suggesting that current memory pipelines may discard useful context that downstream retrieval mechanisms fail to compensate for. Failure analysis shows that performance breakdowns most often manifest at the retrieval stage rather than at utilization. We argue that, under current retrieval practices, i...