{
  "annotation_id": "d19f7660-e6f7-455e-995e-2789af7484fd",
  "date_created": "2026-02-17T05:53:19.480000Z",
  "date_modified": "2026-02-17T05:53:19.480000Z",
  "file_hash": "4746624874752e5ecb9edf0a7037b664101b2bffc74f7527e8294ca82aba9f28",
  "private": false,
  "record": {
    "abstract": "In this paper, I discuss two logics for weighted finite structures: first-order logic with summation (FO(SUM)) and its recursive extension IFP(SUM). These logics originate from foundational work by Gr\\\"adel, Gurevich, and Meer in the 1990s. In recent joint work with Standke, Steegmans, and Van den Bussche, we have investigated these logics as query languages for machine learning models, specifically neural networks, which are naturally represented as weighted graphs. I present illustrative examples of queries to neural networks that can be expressed in these logics and discuss fundamental results on their expressiveness and computational complexity.",
    "arxiv_id": "2601.09381",
    "authors": [
      "Martin Grohe"
    ],
    "categories": [
      "cs.LO",
      "cs.AI",
      "cs.DB"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Query Languages for Machine-Learning Models",
    "url": "https://arxiv.org/abs/2601.09381",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "3b2b1900-5ea2-48a8-970e-3f82c8b619e2",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}