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Pacific Northwest National Laboratory

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Representative Papers

What do Geometric Hallucination Detection Metrics Actually Measure?

Feb 09, 2026

Existing geometric hallucination detection metrics struggle to distinguish specific hallucination types in the absence of ground truth and are highly sensitive to domain shifts. This work addresses these limitations by constructing a synthetic dataset to systematically evaluate the capacity of various geometric statistics to capture key hallucination attributes—such as output correctness, relevance, and coherence—and reveals that different metrics align with distinct hallucination types. Furthermore, the study proposes a simple yet effective normalization strategy that substantially mitigates the impact of domain shift. Experimental results demonstrate that, under multi-domain settings, the proposed approach improves AUROC by 34 percentage points, significantly enhancing the cross-domain robustness of geometric hallucination detection metrics.

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Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning

Jan 20, 2026arXiv.org

This work addresses the challenge that high-fidelity data are scarce and costly, while abundant low-fidelity data lack sufficient accuracy, thereby limiting surrogate model performance. To overcome this, the authors propose a probabilistic multi-fidelity surrogate framework that integrates transfer learning with generative modeling. Built upon a normalizing flow architecture incorporating surjective layers, the model is first pre-trained on extensive low-fidelity data and then fine-tuned with only a small amount of high-fidelity data, enabling efficient knowledge transfer and uncertainty quantification. This approach transcends the dimensional constraints of conventional bijective flows by supporting learnable dimensionality reduction while preserving exact likelihood-based training, marking the first deep integration of generative AI into multi-fidelity modeling. Validated on ballasted railway sleeper and reinforced concrete slab systems, the method achieves highly accurate probabilistic predictions using minimal high-fidelity simulations, significantly outperforming low-fidelity-only baselines.

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NervePool: A Simplicial Pooling Layer

May 10, 2023arXiv.org

Existing graph pooling methods fail to preserve higher-order combinatorial and topological consistency when applied to simplicial complexes—topological data structures capable of encoding high-order relational information. Method: We propose NervePool, the first learnable downsampling layer specifically designed for simplicial complexes. It introduces a vertex-clustering-driven hierarchical coarsening framework that deterministically, differentiably, and topologically awarely compresses from vertices to higher-dimensional simplices via star unions and nerve complex construction. To ensure differentiability and computational efficiency, we integrate GNN-Sinkhorn joint optimization with simplicial adjacency algebra. Contribution/Results: On multiple benchmark tasks, NervePool achieves an average accuracy improvement of 2.3%, significantly enhancing generalization and computational efficiency. It represents the first systematic extension of neural pooling to higher-order topological data, establishing a foundation for deep learning on simplicial complexes.

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Linearized PINN with pretrained nonlinear layers

Sep 13, 2026

该研究提出了一种线性化的物理信息神经网络(lPINN)，通过预训练非线性层并在线性层进行推理，以解决正向和反向微分方程问题，相较于传统方法提高了准确性和效率。

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Linearized PINN with pretrained nonlinear layers

Sep 13, 2026

该研究提出了一种线性化的物理信息神经网络(lPINN)，通过预训练非线性层并在线性层进行推理，以解决正向和反向微分方程问题，相较于传统方法提高了准确性和效率。

0 citationsRead paper