连接点：长时对话中的反思性记忆基准测试
Connecting the Dots: Benchmarking Reflective Memory in Long-Horizon Dialogue
AI总结：
针对现有基准无法衡量从碎片化多模态线索合成高层解释的反思性记忆问题，提出RefMem-Bench基准和REMIND层次框架，通过渐进式证据感知、定位和抽象提升模型反思性记忆能力。
摘要：
尽管长上下文建模取得了显著进展，现有基准仍局限于显式回忆的事实性记忆，未能衡量将碎片化、多模态线索合成为高层解释所需的反思性记忆。为填补这一空白，我们引入了RefMem-Bench，一个用于长时对话中反思性记忆的基准。RefMem-Bench包含26K个带注释的问答实例，涵盖八个反思性记忆维度和三种任务格式，要求模型超越表面检索，从分布在整个交互历史中的证据推断潜在含义。为增强反思性记忆能力，我们提出了反思性记忆归纳（REMIND），一个将反思性记忆视为渐进意义构建的层次框架。REMIND结合了问题条件证据检索、显著性感知定位和抽象级别监督，并使用渐进式反思对齐将高层反思性推理提炼到事实推理路径中。实验表明，RefMem-Bench对当前模型构成了重大挑战，而REMIND通过渐进式证据感知、定位和抽象，持续提高了答案准确性和记忆回忆率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
