图记忆：一种结构化且可解释的多模态嵌入推理框架
Graph Memory: A Structured and Interpretable Framework for Modality-Agnostic Embedding-Based Inference
AI总结：
图记忆通过紧凑的可靠性标注原型区域图表示嵌入空间，结合原型关系编码局部几何和区域模糊性，实现查询证据扩散推理，统一实例检索、原型推理和图扩散，提供更准确和可解释的多模态推理。
摘要：
我们引入图记忆（GM），一种结构化非参数框架，通过紧凑的图表示嵌入空间。GM通过原型关系编码局部几何和区域模糊性，并通过扩散查询证据进行推理，将实例检索、基于原型的推理和图扩散统一在单一归纳且可解释模型中。该框架本质上是模态无关的：在多模态设置中，为每个模态独立构建原型图，通过可靠性感知的后期融合结合其校准预测，实现全切片图像和基因表达谱等异构源的透明整合。在合成基准、乳腺组织病理学（IDC）和多模态AURORA数据集上的实验表明，GM在准确度上与kNN和标签传播相当，同时提供显著更好的校准、更平滑的决策边界和小一个数量级的内存足迹。通过显式建模区域可靠性和关系结构，GM为单模态和多模态领域提供了原则性且可解释的非参数推理方法。
英文摘要：
We introduce Graph Memory (GM), a structured non-parametric framework that represents an embedding space through a compact graph of reliability-annotated prototype regions. GM encodes local geometry and regional ambiguity through prototype relations and performs inference by diffusing query evidence across this structure, unifying instance retrieval, prototype-based reasoning, and graph diffusion within a single inductive and interpretable model. The framework is inherently modality-agnostic: in multimodal settings, independent prototype graphs are constructed for each modality and their calibrated predictions are combined through reliability-aware late fusion, enabling transparent integration of heterogeneous sources such as whole-slide images and gene-expression profiles. Experiments on synthetic benchmarks, breast histopathology (IDC), and the multimodal AURORA dataset show that GM matches or exceeds the accuracy of kNN and Label Spreading while providing substantially better calibration, smoother decision boundaries, and an order-of-magnitude smaller memory footprint. By explicitly modeling regional reliability and relational structure, GM offers a principled and interpretable approach to non-parametric inference across single- and multi-modal domains.
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