{
  "annotation_id": "44e488ad-4c9b-4851-9e34-53a3312b4f6b",
  "date_created": "2026-02-17T05:53:07.964000Z",
  "date_modified": "2026-02-17T05:53:07.964000Z",
  "file_hash": "4343c3925cb1f664c08b595b420f9094b41d83d623c7e4e4a4b0fd9e84826224",
  "private": false,
  "record": {
    "abstract": "EDA development and innovation has been constrained by scarcity of expert engineering resources. While leading LLMs have demonstrated excellent performance in coding and scientific reasoning tasks, their capacity to advance EDA technology itself has been largely untested. We present AuDoPEDA, an autonomous, repository-grounded coding system built atop OpenAI models and a Codex-class agent that reads OpenROAD, proposes research directions, expands them into implementation steps, and submits executable diffs. Our contributions include (i) a closed-loop LLM framework for EDA code changes; (ii) a task suite and evaluation protocol on OpenROAD for PPA-oriented improvements; and (iii) end-to-end demonstrations with minimal human oversight. Experiments in OpenROAD achieve routed wirelength reductions of up to 5.9%, and effective clock period reductions of up to 10.0%.",
    "arxiv_id": "2601.06268",
    "authors": [
      "Amur Ghose",
      "Junyeong Jang",
      "Andrew B. Kahng",
      "Jakang Lee"
    ],
    "categories": [
      "cs.SE",
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Automated QoR improvement in OpenROAD with coding agents",
    "url": "https://arxiv.org/abs/2601.06268",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c34d1eff-90d2-4d71-a3cb-70762c3fdf37",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}