English(EN) WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testing

WOMBAT基准测试在分子数据上测试GNN解释器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员开发了WOMBAT，这是一个新的基准测试，旨在测试图神经网络（GNN）可解释性工具在应用于分子数据时的准确性。WOMBAT包含14个具有手动定义的决策规则的GNN，允许进行地面真实归因分析。该基准测试已在数百万个PubChem分子上进行了验证，并用于评估GNNExplainer、PGExplainer和Integrated Gradients等现有解释器，揭示了特定的失效模式。 AI

影响改进了对分子图AI可解释性工具的评估，可能带来更可靠的AI安全和归因方法。

排序理由该集群描述了一个用于评估AI研究工具（特别是GNN可解释性）的新基准测试和数据集。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Dominik Matuszek, Bartosz Zieli\'nski, Tomasz Danel, Dawid Rymarczyk · 2026-10-03 04:00

WOMBAT：用于分子基准测试和归因测试的白盒Oracle

arXiv:2610.00713v1 Announce Type: new Abstract: When a graph neural network (GNN) explainer produces an unexpected attribution on a molecule, the attribution alone cannot reveal whether the explainer has failed or the model has learned a shortcut. We introduce WOMBAT, a benchmark…

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WOMBAT：用于分子基准测试和归因测试的白盒Oracle

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