MemorAnDA: Memory-Anchor-Driven Architecture for Long-Term Conversational Memory

Answering questions about long conversations can require evidence spread across multiple events, temporal relations, and evolving states. Existing memory systems face a trade-off between reducing query-time computation and preserving historical details. Organizing evidence when each question arrives accommodates its specific information needs, but requires repeated search and selection across questions. Compressing history in advance can reduce query-time work, but must decide what to retain before future questions are known, potentially omitting details needed by later answers. We present Memory-Anchor-Driven Architecture (MemorAnDA; hereafter Memo), which makes history organization reusable without limiting future answers to compressed information. Memory anchors connect organized memories to their original records, allowing the system to use prior organization to find relevant evidence and recover the details needed for each question. This design reduces the need to repeatedly organize history at query time while preserving access to information that compression may omit. Across three matched build/reader model groups, the primary 8K configuration improves answer accuracy over the corresponding LightMem baseline by 1.40–9.80 percentage points on LongMemEval-S and 7.60–10.20 points on LoCoMo.