The article reviews the evolution of memory in language-model agents and highlights limits in current evaluations, which often rely on end-to-end task metrics and treat memory as a black box.
Published on June 26, 2026, the survey examines the evolution of memory in language-model agents toward systems with persistent storage, retrieval, updating, consolidation, and lifecycle governance. It notes that existing evaluations often measure end-to-end tasks and treat memory as a black box.
The analysis may help engineers identify what current agent-memory benchmarks measure—and what they leave out. To check the findings, consult the original article and compare its claims with the methods and metrics of the benchmarks it discusses; the available summary gives no benchmark names or quantitative results.