{"ID":2839244,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16741","arxiv_id":"2511.16741","title":"Fermions and Supersymmetry in Neural Network Field Theories","abstract":"We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism.","short_abstract":"We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa co...","url_abs":"https://arxiv.org/abs/2511.16741","url_pdf":"https://arxiv.org/pdf/2511.16741v1","authors":"[\"Samuel Frank\",\"James Halverson\",\"Anindita Maiti\",\"Fabian Ruehle\"]","published":"2025-11-20T19:00:05Z","proceeding":"hep-th","tasks":"[\"hep-th\",\"cs.LG\"]","methods":"[]","has_code":false}