From Records to Beliefs: NOBLE for Evidence-Grounded Personalized Memory

Long-term personalized agents must infer user states from historical interactions to complete subsequent tasks, yet these states are only partially observable and non-stationary. This requires memory systems to continually revise their understanding of the user, a capability that remains insufficiently supported by methods focused on compressing, organizing, and retrieving historical information. We present NOBLE, an evidence-driven memory system that shifts from managing historical records to maintaining revisable beliefs. NOBLE treats interactions as observational evidence for constructing and updating hypotheses about user states; these hypotheses carry confidence estimates and collectively form the system's evolving belief state. Dynamic themes organize the hypotheses and their associated evidence across emerging user dimensions, guiding both theme-local updates and task-relevant retrieval. This organization enables NOBLE to update its understanding as new evidence arrives while preserving its evidential basis, and to assemble compact context for downstream tasks. We evaluate NOBLE on VitaBench 2.0 for long-term personalized task execution and it achieves 41.0% Avg@4 with GPT-4.1, outperforming all evaluated memory baselines. Evaluation on LongMemEval also demonstrates its ability to support long-term conversational question answering.