{"ID":6537675,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11091","arxiv_id":"2607.11091","title":"Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction","abstract":"A trained molecular property model can be refined at test time by correcting each prediction with the measured labels of the most similar training molecules, a retraining-free procedure we call neighbor fusion; evidential neural networks make it principled by using their aleatoric and epistemic uncertainty to parameterize a Bayesian update. Our main contribution, PG-EVIKAL, learns a property-distance metric to re-rank structurally similar neighbors by their property relevance before fusion, building on EVIKAL (scalar Kalman filter) and GP-EVIKAL (Gaussian process variant handling correlated neighbors). Evaluated on 16 molecular datasets, PG-EVIKAL reduces RMSE relative to the evidential model baseline on 14 of them, with a median reduction of 19.4%, and improves calibration; in sequential-assay scenarios it further incorporates newly measured molecules, refining predictions as they arrive without retraining. This work demonstrates that evidential uncertainty decomposition is not merely a calibration objective but an actionable inference resource that enables test-time refinement of molecular property predictions.","short_abstract":"A trained molecular property model can be refined at test time by correcting each prediction with the measured labels of the most similar training molecules, a retraining-free procedure we call neighbor fusion; evidential neural networks make it principled by using their aleatoric and epistemic uncertainty to parameter...","url_abs":"https://arxiv.org/abs/2607.11091","url_pdf":"https://arxiv.org/pdf/2607.11091v1","authors":"[\"Cameron Gruich\",\"Weichi Yao\",\"Yixin Wang\",\"Bryan Goldsmith\"]","published":"2026-07-13T04:59:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.BM\",\"stat.ML\"]","methods":"[]","has_code":false}