{
  "id": 6025,
  "url": "https://arxiv.org/abs/2604.10300v1",
  "title": "From Helpful to Trustworthy: LLM Agents for Pair Programming",
  "summary": "LLM-based coding agents are increasingly used to generate code, tests, and documentation. Still, their outputs can be plausible yet misaligned with developer intent and provide limited evidence for review in evolving projects. This limits our understanding of how to structure LLM pair-programming workflows so that artifacts remain reliable, auditable, and maintainable over time. To address this gap, this doctoral research proposes a systematic study of multi-agent LLM pair programming that exter",
  "authors": "Ragib Shahariar Ayon",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-11T17:39:57.000Z",
  "fetched_at": "2026-07-14T16:32:11.184Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6025",
  "original_url": "https://arxiv.org/abs/2604.10300v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}