{"ID":5935876,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03039","arxiv_id":"2607.03039","title":"Out-of-distribution Neural Inference in Dynamical Ising Models","abstract":"Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects transferable physical rule learning. We address this question in a controlled inverse problem: reconstructing interaction graphs of a kinetic Ising model from Glauber magnetization trajectories. Across convolutional, graph, Transformer, and hybrid architectures, we find that data-driven training produces distinct and reproducible statistical strategies under topology and temperature shifts. Edge-population diagnostics reveal that Transformer-based models tend to preserve the link density of the training ensemble, whereas convolutional models can collapse toward sparse- or no-link predictions that appear out-of-distribution stable by exploiting the majority no-link class. Thus, high in-distribution accuracy and apparent out-of-distribution robustness do not necessarily imply a learned dynamics-to-structure rule. Instead, neural reconstruction can be governed by architecture-dependent statistical priors. Our results identify a concrete failure mode of standard data-driven learning in physical inverse problems and motivate rule-guided principles for machine-learning-assisted scientific discovery.","short_abstract":"Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects transferable physical rule learning. We address this question in a controlled inverse problem: reconstructing interaction graphs of a kineti...","url_abs":"https://arxiv.org/abs/2607.03039","url_pdf":"https://arxiv.org/pdf/2607.03039v1","authors":"[\"Yuan-Bin Zhu\",\"Shuang Qiao\",\"Shi-Ju Ran\"]","published":"2026-07-03T07:31:30Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cond-mat.stat-mech\"]","methods":"[\"Transformer\"]","has_code":false}