{
  "id": 19117,
  "url": "https://arxiv.org/abs/2608.12768",
  "title": "A Generative Framework for the Creation of Multi-Attribute Geographically-Explicit Synthetic Population",
  "summary": "arXiv:2608.12768v1 Announce Type: new Abstract: Generating multi-attribute synthetic populations with realistic joint distributions and geographic variation is a foundational requirement for geo-simulation techniques, such as micro-simulation and agent-based modeling. However, it remains challenging for existing methods to reconstruct region-specific joint distributions from aggregated-level data alone. Thus, we propose a hierarchical diffusion-based generative framework that utilizes a realisti",
  "authors": "Jinlin Wu, Si Qiao, Yi Liu, Fuzhen Yin, Na Jiang",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-14T04:00:00.000Z",
  "fetched_at": "2026-08-14T05:10:49.168Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/19117",
  "original_url": "https://arxiv.org/abs/2608.12768",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}