{
  "id": 19184,
  "url": "https://arxiv.org/abs/2608.13043v1",
  "title": "From Local Mismatch to Global Impact: Optimizing Cache Reuse Policy for Efficient Diffusion",
  "summary": "Diffusion models have achieved dominant performance in visual generation but suffer from substantial inference overhead. While cache-based acceleration has emerged as a promising solution, existing policies rely on local similarity heuristics, which we identify as being significantly misaligned with final generation quality. This discrepancy stems from the non-uniform propagation and accumulation of errors along the denoising trajectory. To address this, we propose Global-Impact Cache (GCache).",
  "authors": "Xichen Ye, Yifan Wu, Zhikang Xie, Xiangyu Yue, Cheng Jin, Weizhong Zhang",
  "category": "research",
  "topics": "regulation,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-13T10:08:47.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/19184",
  "original_url": "https://arxiv.org/abs/2608.13043v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}