{
  "abstract": "Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has focused on the emergence of a global low-dimensional representation. We show that this picture is incomplete. In regression problems with clustered data, we demonstrate that multilayer perceptrons (MLPs) develop specialized neurons: individual neurons become strongly aligned with specific features relevant to a particular region of the input space. A substantial fraction of these neurons exhibit monosemanticity: they activate predominantly on inputs from the corresponding cluster. Thus, rather than learning a single global low-dimensional representation, MLPs learn a collection of local low-dimensional representations. Furthermore, we show that this ability to specialize gives MLPs a provable data-efficiency advantage over feature-learning methods based on a global low-dimensional representation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.24007",
  "author_total": 4,
  "authors": [
    {
      "name": "Amirhesam Abedsoltan"
    },
    {
      "name": "Enric Boix-Adsera"
    },
    {
      "name": "Fivos Kalogiannis"
    },
    {
      "name": "Mikhail Belkin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/avc1by6h25999.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.24007",
  "fulltext_url": null,
  "id": "avc1by6h25999",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.24007",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Neural Networks and Applications"
    ]
  ],
  "openalex": "W7204238824",
  "pdf_url": "https://arxiv.org/pdf/2608.24007",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/avc1by6h25999.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:59Z",
      "native_id": "2608.24007",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.24007"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Revenge of Monosemanticity: Neuron Specialization as a New Form of Feature Learning in MLPs",
  "title_text": "Revenge of Monosemanticity: Neuron Specialization as a New Form of Feature Learning in MLPs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:59Z",
  "url": "https://research.draconitic.com/paper/avc1by6h25999",
  "venue": "arXiv",
  "versions": []
}