{
  "annotation_id": "1dc71370-c9af-4fcc-a27d-f7503f10f54a",
  "date_created": "2026-02-17T05:53:23.741000Z",
  "date_modified": "2026-02-17T05:53:23.741000Z",
  "file_hash": "15b4d9af077c695cf087c7394d4f6203b009d67b696a30f105aa29bbe9f04e35",
  "private": false,
  "record": {
    "abstract": "Comprehending genomic information is essential for biomedical research, yet extracting data from complex distributed databases remains challenging. Large language models (LLMs) offer potential for genomic Question Answering (QA) but face limitations due to restricted access to domain-specific databases. GeneGPT is the current state-of-the-art system that enhances LLMs by utilizing specialized API calls, though it is constrained by rigid API dependencies and limited adaptability. We replicate GeneGPT and propose GenomAgent, a multi-agent framework that efficiently coordinates specialized agents for complex genomics queries. Evaluated on nine tasks from the GeneTuring benchmark, GenomAgent outperforms GeneGPT by 12% on average, and its flexible architecture extends beyond genomics to various scientific domains needing expert knowledge extraction.",
    "arxiv_id": "2601.10581",
    "authors": [
      "Kimia Abedini",
      "Farzad Shami",
      "Gianmaria Silvello"
    ],
    "categories": [
      "cs.AI",
      "cs.IR"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "From Single to Multi-Agent Reasoning: Advancing GeneGPT for Genomics QA",
    "url": "https://arxiv.org/abs/2601.10581",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "2e8c0467-8a3a-4db4-952d-3f8c1ef75c82",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}