OMEGA: A Utility-Aware Lifecycle Co-Design for Long-Term Memory in LLM Agents

Long-term memory systems for LLM agents face a fundamental tension: rich extraction and retrieval improve evidence coverage but incur prohibitive lifecycle costs. Controlled analyses on LoCoMo and LongMemEval reveal three design principles: (1) preserve complementary evidence without proliferating memory types; (2) decouple concise matching keys from coherent answer-bearing payloads; (3) expand retrieval coverage before folding evidence into a bounded solver context. We instantiate these principles in Omega, which combines session-aligned typed projection, key-payload separated maintenance, and bounded multi-route retrieval with late parent folding. Across three benchmarks, Omega achieves state-of-the-art performance, leading the strongest baseline by an average of 3.59 points while using 25–86% less solver context and cutting write-side calls by 81–88% on LoCoMo. Notably, Omega transfers to the held-out PersonaMem-128K, demonstrating robust cross-benchmark generalization.