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

• URL: http://arxiv.org/abs/2606.01223v1
• Date: Sun, 31 May 2026 13:16:02 GMT
• Title: Connecting the Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue
• Abstract summary: RefMem-Bench is a benchmark for reflective memory in long-horizon dialogue.<n>REMIND couples question-conditioned evidence retrieval, salience-aware grounding, and abstraction-level supervision.
• Score: 28.424707470781428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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