{
  "id": 16949,
  "url": "https://arxiv.org/abs/2608.04331v1",
  "title": "IntentLint: Supporting Intent Scaffolding and Prompt-time Linting in Human-AI Collaborative Data Analysis",
  "summary": "In human-AI collaborative data analysis, as analyses rapidly evolve, the artifacts meant to capture shared understanding often become incomplete or difficult to interpret, leading to undocumented assumptions, cross-user misaligned intent, context-poor prompts, and unwanted agent behaviors. To address these challenges, we introduce a rule-based coordination layer with two interaction mechanisms, intent scaffolding and prompt-time linting, that make analytic intent explicit and actionable during h",
  "authors": "Felicia Li Feng, Jian Zhao, Anamaria Crisan",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T01:17:45.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "x-arxiv-cs-hc",
  "source_name": "arXiv cs.HC",
  "source_homepage": "https://arxiv.org/list/cs.HC/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16949",
  "original_url": "https://arxiv.org/abs/2608.04331v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}