{
  "id": 5084,
  "url": "https://arxiv.org/abs/2605.15208v1",
  "title": "Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels",
  "summary": "Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deployment, yet the impact of this compression on model quality remains poorly understood. Existing studies typically compare only two conditions (full-precision vs. a single quantized variant), rely on aggregate bias metrics, and evaluate a single model family, making it impossible to distinguish gradual degradation from threshold-dependent safety failu",
  "authors": "Plawan Kumar Rath, Rahul Maliakkal",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-02T05:41:47.000Z",
  "fetched_at": "2026-07-14T16:31:31.209Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5084",
  "original_url": "https://arxiv.org/abs/2605.15208v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}