{
  "id": 11857,
  "url": "https://arxiv.org/abs/2607.15830v1",
  "title": "RTL-Sequencer: Towards Scalable RTL Timing Prediction with the Sequence-based Paradigm",
  "summary": "Accurate timing prediction at the register-transfer level (RTL) is a longstanding challenge in design automation. Existing graph-based methods struggle with limited receptive fields, high complexity, and a lack of signal directionality. We present RTL-Sequencer, a novel sequence-based paradigm that enables scalable RTL timing prediction via linearizing logic cones by breadth-first traversal and applying modern linear sequence models. Furthermore, sequence models are customized by four synergisti",
  "authors": "Ziyan Guo, Wenji Fang, Wenkai Li, Yuchao Wu, Shang Liu, Zhiyao Xie",
  "category": "research",
  "topics": "jobs-economy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-17T10:40:38.000Z",
  "fetched_at": "2026-07-20T05:10:09.534Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/11857",
  "original_url": "https://arxiv.org/abs/2607.15830v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}