{
  "id": 1287,
  "url": "https://arxiv.org/abs/2606.08676v1",
  "title": "Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks",
  "summary": "AI coding assistants have significantly improved developer productivity by automatically suggesting code that aligns with user intent, and many of these tools are now integrated directly into Integrated Development Environments (IDEs). Developers interact with code in two distinct cognitive modes: Flow and Command. While developers require tools that directly complete or infill code in unfinished programs during Flow mode, they also need tools that can comprehend intentions expressed as natural-",
  "authors": "Shi Ying Chang, Chiok Yew Ho, Yichen Li, Yintong Huo",
  "category": "research",
  "topics": "jobs-economy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-07T15:24:30.000Z",
  "fetched_at": "2026-07-14T14:15:07.847Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1287",
  "original_url": "https://arxiv.org/abs/2606.08676v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}