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Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Hypergraph Foundation Model.

Yue Gao, Yifan Feng, Shiquan Liu

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    Summary
    This summary is machine-generated.

    This study introduces Hyper-FM, a foundation model for hypergraphs, enhancing knowledge extraction. Domain diversity is key for scaling hypergraph foundation models, outperforming methods that only increase data size.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Graph Theory

    Background:

    • Hypergraph neural networks (HGNNs) model complex relationships using hyperedges.
    • Developing foundation models for hypergraphs is challenging due to unique data structures.
    • Existing methods struggle with high-order relationships and intricate structural information.

    Purpose of the Study:

    • Introduce Hyper-FM, a novel hypergraph foundation model.
    • Enable multi-domain knowledge extraction from text-attributed hypergraphs.
    • Advance research at the intersection of HGNNs and large language models (LLMs).

    Main Methods:

    • Developed Hierarchical High-Order Neighbor Guided Vertex Knowledge Embedding for vertex features.
    • Implemented Hierarchical Multi-Hypergraph Guided Structural Knowledge Extraction for structural information.
    • Curated 11 new text-attributed hypergraph datasets.

    Main Results:

    • Hyper-FM achieved an approximate 13.4% performance improvement over baseline methods.
    • Demonstrated the first scaling law for hypergraph foundation models.
    • Showed that domain diversity significantly enhances model performance.

    Conclusions:

    • Hyper-FM effectively extracts knowledge from hypergraphs.
    • Domain diversity is crucial for scaling hypergraph foundation models.
    • The proposed methods and datasets will drive future research in HGNNs and LLMs.