{
  "id": 17391,
  "url": "https://arxiv.org/abs/2608.06246v1",
  "title": "A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance",
  "summary": "Post-training adaptation has become central to modern machine learning practice and includes techniques such as retraining, fine-tuning, parameter-efficient adaptation, alignment, retrieval augmentation, model editing, unlearning, calibration, and Multimodal Instruction Tuning. However, the literature remains fragmented across technique families, model classes, and deployment contexts, making it difficult to compare methods or describe how a trained model has been modified. This survey synthesiz",
  "authors": "Fardin Afdideh, Fernando Seoane, Farhad Abtahi",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T16:32:26.000Z",
  "fetched_at": "2026-08-07T05:10:58.501Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17391",
  "original_url": "https://arxiv.org/abs/2608.06246v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}