Rui Yu

Long-horizon agents must preserve past experience before knowing what future queries will require. Compact memory makes growing histories efficient to reuse by distilling experience into reusable abstractions, while raw history preserves fine-grained evidence for faithful grounding. Because future queries require different levels of detail, the appropriate memory granularity cannot be reliably fixed when memory is written. We therefore argue that memory granularity should be determined when the future information need becomes known. We introduce \textbf{TierMem}, a provenance-linked two-tier memory framework that allocates memory granularity according to evidence sufficiency. TierMem retrieves compact memory by default, uses a learned router to identify when finer-grained evidence is required, and follows provenance links to recover relevant raw evidence. Recovered evidence can be consolidated into compact memory for future reuse. Across two long-horizon memory benchmarks, TierMem recovers much of the quality enabled by raw evidence while substantially reducing inference cost. Further analyses show that provenance improves evidence recovery and that consolidation progressively increases the share of queries served by compact memory.