{
  "annotation_id": "89c0c3ba-0031-492a-ae35-73069533b1e4",
  "date_created": "2026-02-17T05:53:12.480000Z",
  "date_modified": "2026-02-17T05:53:12.480000Z",
  "file_hash": "a2cfc496aba007d23ac9714106078d729a93e9d582bfb45f40a111385bdb5392",
  "private": false,
  "record": {
    "abstract": "We introduce Tab-TRM (Tabular-Tiny Recursive Model), a network architecture that adapts the recursive latent reasoning paradigm of Tiny Recursive Models (TRMs) to insurance modeling. Drawing inspiration from both the Hierarchical Reasoning Model (HRM) and its simplified successor TRM, the Tab-TRM model makes predictions by reasoning over the input features. It maintains two learnable latent tokens - an answer token and a reasoning state - that are iteratively refined by a compact, parameter-efficient recursive network. The recursive processing layer repeatedly updates the reasoning state given the full token sequence and then refines the answer token, in close analogy with iterative insurance pricing schemes. Conceptually, Tab-TRM bridges classical actuarial workflows - iterative generalized linear model fitting and minimum-bias calibration - on the one hand, and modern machine learning, in terms of Gradient Boosting Machines, on the other.",
    "arxiv_id": "2601.07675",
    "authors": [
      "Kishan Padayachy",
      "Ronald Richman",
      "Mario V. W\u00fcthrich"
    ],
    "categories": [
      "cs.LG",
      "q-fin.RM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data",
    "url": "https://arxiv.org/abs/2601.07675",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6cf05700-d4a8-490e-b8ed-186890e9c205",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}