RippleMem Builds Agent Memory From Cues Instead of One-Shot Retrieval, Cutting Graph Construction Cost ~30x

arXiv 2608.13334·high signalXBlueskyLinkedInCopy link

RippleMem (arXiv 2608.13334, 2026-08-13) argues the bottleneck in long-term agent memory is not storage but recovering evidence scattered across many interactions — flat retrieval returns isolated records, and graph memory systems are expensive to build. It stores history as cue-rich episodic units in an event-centric graph, recalls anchors via hybrid cues, then expands along semantic and structural associations so initially recalled memories become cues for finding the rest. On LoCoMo and LongMemEval-S it improves LLM-as-a-Judge accuracy by 3.95% and up to 11.87% respectively while reducing graph construction cost roughly 30x.