Evaluating Memory Structure in LLM Agents

ArXi:2602.11243v2 Announce Type: replace Modern LLM-based agents and chat assistants rely on long-term memory frameworks to reusable knowledge, recall user preferences, and augment reasoning. As researchers create complex memory architectures, it becomes increasingly difficult to analyze their capabilities and guide future memory designs. Most long-term memory benchmarks focus on simple fact retention, multi-hop recall, and time-based changes.