cond-mat.dis-nn · 2026-06-23 · No. 32

Disordered Systems and Neural Networks, 2026-06-23.

2 new papers in cond-mat.dis-nn. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Approximating velocity fields with planted attractors via Neural-ODEs for classification purposes

    Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni, Lorenzo Chicchi, Diego Febbe, Raffaele Marino

    cond-mat.dis-nn · cs.LG

    In this work, Neural ODEs equipped with a curated collection of equilibrium points have been successfully employed for classification tasks.The planted attractors serve as indicators for the target classes, while the velocity field leveraging the universal approximation capabilities of the architecture shapes the dynamical landscape.This process defines the basins of attraction of the trained model, effectively directing each input provided...

    arxiv.org/abs/2606.23550 · PDF

  2. 02

    Scalable Physics-Inspired Transformers for Spin Glasses

    Lu Zhong, Wenli Duan, Jing Liu, Pan Zhang, Ying Tang

    cond-mat.dis-nn · cond-mat.stat-mech · cs.LG

    Efficient sampling of the Boltzmann distribution in frustrated spin glasses is central to statistical mechanics and combinatorial optimization. Despite advances in machine-learning-based approaches, two issues persist: limited understanding of why variational models fail to benefit from increased scale, unlike the monotonic scaling law of large language models; and high computational cost on large systems that negates advantages over...

    arxiv.org/abs/2606.22984 · PDF

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