{
  "id": 16098,
  "url": "https://arxiv.org/abs/2608.02407v1",
  "title": "Antares: Foundation Models for Agentic Vulnerability Localization",
  "summary": "Vulnerability localization is a fundamental step in software security, requiring models to reason over large codebases and iteratively identify vulnerable implementations. We present Antares, a family of compact language models (350M, 1B, and 3B parameters) for agentic vulnerability localization. Based on IBM Granite base models, Antares is trained through a two-stage pipeline that combines supervised fine-tuning on cybersecurity reasoning and repository exploration data with reinforcement learn",
  "authors": "Supriti Vijay, Aman Priyanshu, Didier Chapoteau, Arthur Goldblatt, Jianliang He, Kimia Majd, Fraser Burch, Baturay Saglam, Takahiro Matsumoto, Zhuoran Yang, Amin Karbasi",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T15:49:14.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "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/16098",
  "original_url": "https://arxiv.org/abs/2608.02407v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}