{
  "id": 7029,
  "url": "https://arxiv.org/abs/2603.18507v1",
  "title": "Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM",
  "summary": "Persona prompting can steer LLM generation towards a domain-specific tone and pattern. This behavior enables use cases in multi-agent systems where diverse interactions are crucial and human-centered tasks require high-level human alignment. Prior works provide mixed opinions on their utility: some report performance gains when using expert personas for certain domains and their contribution to data diversity in synthetic data creation, while others find near-zero or negative impact on general u",
  "authors": "Zizhao Hu, Mohammad Rostami, Jesse Thomason",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-19T05:28:21.000Z",
  "fetched_at": "2026-07-14T16:32:54.535Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7029",
  "original_url": "https://arxiv.org/abs/2603.18507v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}