{
  "id": 3331,
  "url": "https://arxiv.org/abs/2606.01292v1",
  "title": "What Makes a Strong Model? A Unified Spectral Analysis of Knowledge Transfer over High-dimensional Linear Regression",
  "summary": "Teacher-Student Knowledge Transfer (KT) is ubiquitous in modern machine learning, ranging from classical model compression via Knowledge Distillation (KD) to the emergent phenomenon of Weak-to-Strong (W2S) generalization. While existing studies offer isolated insights, a unified theoretical framework explaining the efficacy of KT across these disparate regimes remains lacking. In this work, we establish a unified spectral analysis of SGD dynamics in high-dimensional linear regression, elucidatin",
  "authors": "Wendao Wu, Fangqing Zhang, Haihan Zhang, Cong Fang",
  "category": "research",
  "topics": "bias-fairness,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-31T15:24:52.000Z",
  "fetched_at": "2026-07-14T16:30:09.962Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3331",
  "original_url": "https://arxiv.org/abs/2606.01292v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}