From Memory Access to Evidence Satisfaction: Diagnosing and Resolving Demand–Evidence Misalignment

Long-term memory systems are typically optimized to preserve and retrieve records relevant to a later query. Yet relevance is an item-level property, whereas answering is a set-level requirement: the retrieved evidence must jointly establish the facts, relations, temporal conditions, and state constraints required by the question. We characterize this challenge as demand–evidence misalignment and introduce MemPath, an interventional diagnostic framework that traces the transformation from historical information to answer-supporting evidence across the memory lifecycle. Across representative systems, MemPath reveals that improving individual memory stages does not necessarily ensure sufficient evidence for answering, as failures can arise from insufficient access, incomplete demand coverage, or ineffective evidence utilization. Guided by this diagnosis, we propose BiDE-Mem, a demand-guided evidence construction framework that represents questions as typed demands, retrieves and organizes supporting evidence, and aligns the constructed evidence with these demands before answer generation. BiDE-Mem improves accuracy by 6.23 points on LoCoMo and achieves 91.80% overall accuracy on LongMemEval, with particularly strong results on multi-hop and multi-session reasoning. Ablations and evidence-level analyses attribute these gains to improved demand coverage and evidence organization rather than evidence quantity alone. Our results highlight that effective long-term memory requires not only accessing relevant information, but also constructing evidence that satisfies the requirements of the current question. We release our code at https://anonymous.4open.science/r/BIDE-8507.