JustMem: Just-Enough Memory Access for Long-Term Conversations

RESUMO

Efficient long-term conversational memory requires retrieving sufficient evidence without indiscriminately expanding the context presented to language model. This is challenging because relevant evidence may be distributed across multiple sessions, while compression may discard details needed for answering. Different queries therefore require different forms of memory access. To capture these demands, we formulate memory access along two dimensions: discovery breadth, which controls how broadly evidence is searched, and reading fidelity, which controls whether evidence is read in compact form or recovered from the original conversation. Based on this formulation, we introduce JustMem, which stores conversation history as compact atomic memories and adapts memory access dimensions to each query. Specifically, LOOKUP handles local evidence, COMPOSE broadens discovery breadth, and REPLAY increases reading fidelity for fidelity-sensitive evidence. On LoCoMo and LongMemEval-S, JustMem achieves the highest mean accuracy in retrieval recall among compared memory systems while using substantially fewer generative-model tokens for construction and inference.