{
  "id": 3473,
  "url": "https://arxiv.org/abs/2605.30632v1",
  "title": "Rationalize: Shared Semantic Reasoning for Human-AI Alignment",
  "summary": "We introduce Rationalize, a role-pair framework for shared semantic reasoning between humans and AI models in data-driven sensemaking. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as a series of complementary role pairs (Explorer-Guide, Investigator-Informant, Teacher-Student, Judge-Advocate) operating in a shared reasoning space. In this space, human analysts and AI models (such as LLMs) make purposes, questions, assumptions, evidence, ",
  "authors": "Aritra Dasgupta, Naga Datha Saikiran Battula, Avina Nakarmi, Sohom Sen, Subhodeep Ghosh, Xun Song",
  "category": "research",
  "topics": "safety-alignment,children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-28T22:34:28.000Z",
  "fetched_at": "2026-07-14T16:30:18.853Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3473",
  "original_url": "https://arxiv.org/abs/2605.30632v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}