{
  "id": 95,
  "url": "https://arxiv.org/abs/2607.08255v1",
  "title": "Compete Then Collaborate: Frontier AI Teachers Build a Verifiable Curriculum to Improve a Coding Student Beyond Imitation",
  "summary": "Large language models increasingly serve as teachers generating training data for smaller students. Prior multi-teacher knowledge distillation methods merge outputs without determining which frontier model teaches best, often relying on an LLM judge biased toward its own outputs. We introduce a compete-then-collaborate framework where four frontier AI teachers (Claude, Codex-GPT, Grok, Gemini) are ranked head-to-head by an execution-based judge (unit tests and stdin-stdout checks) with fairness ",
  "authors": "Miseong Shawn Kim",
  "category": "research",
  "topics": "bias-fairness,children-education",
  "orgs": "google,xai",
  "regions": null,
  "published_at": "2026-07-09T09:00:52.000Z",
  "fetched_at": "2026-07-14T14:14:15.667Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/95",
  "original_url": "https://arxiv.org/abs/2607.08255v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}