{
  "id": 18802,
  "url": "https://arxiv.org/abs/2608.11537v1",
  "title": "Generative Semantic Segmentation via an Observable Semantic-Image Interface and Hierarchical Generator Evidence Alignment",
  "summary": "Generative semantic segmentation exposes structured predictions as images, but direct color decoding is susceptible to color drift and boundary mixing, whereas latent-feature decoders that predict a separate output distribution may relegate the rendered image to an intermediate visualization. We present Semantic Prism, a conditional semantic-image generation-and-refinement framework with deterministic inference. A diffusion-distilled one-step generator renders a semantic RGB image; per-pixel dis",
  "authors": "Weize Cai, Yongqi Dong, Zhida Shao, Zixin Fu",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-12T01:00:04.000Z",
  "fetched_at": "2026-08-13T05:10:37.786Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18802",
  "original_url": "https://arxiv.org/abs/2608.11537v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}