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A data-anchored penalty is proposed that weights prediction errors by the residual Jacobian at the clean training sample and normalizes the resulting magnitude, which removes the incentive to relocate to less sensitive states without changing the model architecture or sampling procedure.
Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high cost of generating large-scale training datasets. Consequently, pre-training on massive datasets of related PDE dynamics has emerged as a critical alternative to enhance...
GoldiMask is introduced, which selects tokens to reveal as context by approximately maximizing a submodular objective and weights the remaining targets according to how they benefit from the selected context and their remaining learning potential.
Discrete diffusion models have emerged as a powerful paradigm for solving combinatorial optimization (CO) problems on graphs by learning to sample high-quality solutions. A common inference-time approach is to generate multiple candidate solutions independently and return the best-performing sample, improving solution...
SymDrift is a framework that makes the drifting field itself symmetry-aware, and introduces two complementary strategies: a symmetrized drift in coordinate space based on optimal alignment, and a $G$-invariant embedding that removes symmetry ambiguity by construction.
Block normalization generally improves Adam and closes part of its gap to Muon across molecular force prediction on the rMD17 and MD22 datasets, QM9 molecular property prediction, and charged particle dynamics.
This work proposes Teacher Rollout Extension (TREX), a knowledge distillation framework that transfers the predictive capability of a pretrained foundation model into a compact and efficient student that can match or surpass the teacher's accuracy while reducing the number of parameters by several orders of magnitude a...
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