Finally a good paper testing whether agent memory needs an LLM at all. Production memory stacks spend extra model calls on summarizing inter…

Finally a good paper testing whether agent memory needs an LLM at all. Production memory stacks spend extra model calls on summarizing interactions, writing records, and reranking retrievals. Every one of those calls costs tokens and latency, and the summaries quietly discard the evidence you later need. Zero-Mem removes generation from every memory step. Only the final question-answering reader ever invokes an LLM. It keeps original interaction traces as the record and indexes them two ways. An entity-context graph exposes connections across sessions. A temporal hierarchy preserves conversational locality and session state. For each query it weighs both views, retrieves from both, and follows their structure to recover supporting relations or surrounding context. Deterministic calibration then discards conflicting evidence before the reader answers. At matched reader and context budget, memory-operation time cost drops 57.6% against the fastest compared baseline, with competitive accuracy on long-memory and long-context QA. Paper: https://arxiv.org/abs/2607.29377 Track more trending AI papers in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-08-03