{
  "annotation_id": "b038887f-9102-440b-8bb0-743387083646",
  "date_created": "2026-02-17T05:53:15.436000Z",
  "date_modified": "2026-02-17T05:53:15.436000Z",
  "file_hash": "d0d4223a2ab28bcf6bd82eb13d808a850093948519ac426ffb3ffef2c0acba55",
  "private": false,
  "record": {
    "abstract": "Large AI Model (LAM) have been proposed to applications of Non-Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific trainings. In this paper, we propose a Deep Reinforcement Learning (DRL) agent that is guided by a Large Language Model (LLM). The LLM operates as a high level coordinator that generates textual guidance that shape the reward of the DRL agent during training. The results show that the LAM-DRL outperforms the traditional DRL by 40% in nominal weather scenarios and 64% in extreme weather scenarios compared to heuristics in terms of throughput, fairness, and outage probability.",
    "arxiv_id": "2601.08254",
    "authors": [
      "Abdikarim Mohamed Ibrahim",
      "Rosdiadee Nordin"
    ],
    "categories": [
      "cs.AI",
      "cs.SY",
      "eess.SY"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks",
    "url": "https://arxiv.org/abs/2601.08254",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "a256cf3f-feba-4020-b980-393a348eb6a3",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}