CoDiMem: Evolving Agent Memory through Intervention-Grounded Diagnostic Learning

LLM agents increasingly rely on external memory to reuse experience across tasks, motivating recent approaches that evolve memory architectures from interaction experience. However, architecture evolution requires repeatedly deciding what to change next, while existing methods largely use previous trials to select candidate architectures rather than to improve the diagnostic policy that guides subsequent search. We introduce CoDiMem, a self-evolving agentic memory system that reuses each architecture trial in two complementary ways: as evidence for selecting the next architecture and as a factual trial record for updating diagnostic guidance, thereby allowing subsequent diagnostic decisions to build on what earlier trials actually established rather than reasoning from each new trajectory in isolation. Across AppWorld and MemoryArena with two LLM backbones, CoDiMem improves upon its paired starting architectures while reducing search cost; on AppWorld with StepFun 3.7 Flash, held-out accuracy increases from 59.03% to 69.44%. Ablations further show that removing diagnostic-guidance updates degrades both task performance and search efficiency, supporting the value of learning from accumulated architecture trials.