Entity indexing preserves raw dialogue for agent long-term memory

Agent long-term memory no longer has to rely on lossy summaries: EnSIMem builds indexes of the form [entity][entity_type][property: value], each preserving source turns and temporal information, with the code open-sourced on GitHub.

Existing memory systems often compress interactions into generic summaries or retrieve anonymous text chunks, making it hard for an agent to identify the right entity, property and evidence. EnSIMem organizes interactions offline into theme-coherent episodes and, online, decomposes each request into evidence requirements and answers from the index.

Xuanyu Meng and five coauthors self-report that the system achieves high answer accuracy while keeping contexts compact; the accompanying repository shows the experiments run on the LoCoMo and LongMemEval long-term conversational memory benchmarks, with ablation scripts included, but the abstract names no baseline or number. The preprint was submitted on September 23, and the performance claim awaits the paper body and independent reproduction.