[2511.14961] Graph Memory: A Structured and Interpretable Framework for Modality-Agnostic Embedding-Based Inference

Computer Science > Machine Learning arXiv:2511.14961 (cs) [Submitted on 18 Nov 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Graph Memory: A Structured and Interpretable Framework for Modality-Agnostic Embedding-Based Inference Authors:Artur A. Oliveira, Mateus Espadoto, Roberto M. Cesar Jr., Roberto Hirata Jr View a PDF of the paper titled Graph Memory: A Structured and Interpretable Framework for Modality-Agnostic Embedding-Based Inference, by Artur A. Oliveira and 3 other authors View PDF HTML (experimental) Abstract: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 Spreadin...