DUNE：Keeping Derived Memories Consistent with Changing Facts

Research on long-term memory for agents increasingly studies how to replace an obsolete fact with its updated value. However, this overlooks a deeper problem, that is, updating a single stored fact can invalidate downstream conclusions, answers, or plans derived from it, even when their revised values are never explicitly stated. Drawing an analogy to materialized views and incremental view maintenance in databases, we define such downstream conclusions as derived memories. Derived memory maintenance requires revising the source evidence and any downstream derived memories whose validity or value depends on it. Based on this, we introduce DRM-Bench, a collection of 3,200 two-session scenarios for evaluating derived memory maintenance across semantic, episodic, and procedural memory. To address this problem, we propose DUNE (Derived memory Updating through Networked Evidence), which maps evidence to nodes and dependencies to edges, then propagates source updates over the resulting graph. DUNE achieves the best source update (84.8%), and derived update (73.6%) among the tested methods on DRM-Bench, and improves averaged F1, LLM Judge, SourceUpdate accuracy, and DerivedUpdate accuracy by 9.6, 14.8, 22.2, and 22.5 points, respectively.