Connecting-the-Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue

Despite substantial progress in long-context modeling, existing benchmarks remain confined to factual memory for explicit recall, failing to measure the reflective memory required to synthesize fragmented, multimodal cues into high-level interpretations. To address this gap, we introduce **RefMem-Bench**, a benchmark for reflective memory in long-horizon dialogue. **RefMem-Bench** contains 26K annotated QA instances with eight reflective-memory dimensions and three task formats, requiring models to move beyond surface-level retrieval and infer latent meanings from evidence distributed across interaction histories. To enhance reflective memory capability, we propose **RE**flective **M**emory **IND**uction (**REMIND**), a hierarchical framework that treats reflective memory as progressive meaning construction. **REMIND** couples question-conditioned evidence retrieval, salience-aware grounding, and abstraction-level supervision, and uses Progressive Reflective Alignment to distill high-level reflective reasoning into the factual inference pathway. Experiments show **RefMem-Bench** poses a substantial challenge to current models, while **REMIND** consistently improves both answer accuracy and memory recall through progressive evidence perception, grounding, and abstraction.