{
  "id": 1844072,
  "title": "ARASH: Adaptive Retrieval And Shot Selection for Tabular Prediction",
  "url": "https://urgent.news/2026/08/18/arash-adaptive-retrieval-and-shot-selection-for-tabular-prediction",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-18T14:48:56.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.17856v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approaches for adapting them to the tabular domain. A prevalent strategy involves training or fine-tuning specialized Tabular Foundation Models (TFMs) such as TabPFN. However, TFMs require substantial computational resources, and frequent model retraining is often…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}