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This work builds four modules (for drug-discovery chemistry, materials science, machine learning, and mineral geochemistry) in the ArticleMiner framework, and evaluates them on 163 papers, including a new geochemistry benchmark with expert-curated ground truth.
QUEST, which adds no trainable parameters to the standard confidence-distribution learning pipeline, improves confidence prediction and link prediction on six of eight metric-dataset pairs over prior methods and matches the previous best on the remaining two, while removing the instability spike observed on dense graph...
The results characterize the expressive power of global-attention GFMs and provide an encoder-agnostic diagnostic for detecting 1-WL-induced representation equivalence.
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