{
  "id": 4041,
  "url": "https://arxiv.org/abs/2605.27419v1",
  "title": "APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents",
  "summary": "LLM-agent simulation offers a flexible computational tool for studying population response trajectories that depend on scenario events, memory, demographics, and evolving social context. However, full multi-round simulation scales linearly with both population size and horizon, requiring every agent to query the LLM at every round. We propose Adaptive Prototype Simulation (APS), a framework that reframes scalable LLM-based simulation as a recurrent oracle-allocation problem. APS retains the desi",
  "authors": "Quan Zheng, Yan Gao, Shaobin He, Haoxiang Guan, Yuanhe Tian, Jie Feng et al.",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-19T08:45:41.000Z",
  "fetched_at": "2026-07-14T16:30:41.583Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4041",
  "original_url": "https://arxiv.org/abs/2605.27419v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}