Connecting the Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue
For researchers in long-context modeling and dialogue systems, this work addresses the lack of benchmarks for reflective memory, which is crucial for tasks requiring inference beyond factual recall.
The paper introduces RefMem-Bench, a benchmark for reflective memory in long-horizon dialogue with 26K QA instances, and proposes REMIND, a hierarchical framework that improves answer accuracy and memory recall by synthesizing fragmented cues into high-level interpretations.
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 REflective Memory INDuction (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.