{
  "id": 3310,
  "url": "https://arxiv.org/abs/2606.01640v1",
  "title": "MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation",
  "summary": "Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features. Existing paradigms, including deep generative models, LLM-based methods, and traditional heuristics, struggle to satisfy the complex demands of this task while simultaneously maintaining interpretability, behavioral plausibility, population-level distributional alignment, and inference efficiency. To bridge this gap, we introduce MobEvolve, the first agentic self-evolving heuri",
  "authors": "Junlin He, Yihong Tang, Tong Nie, Ao Qu, Yuebing Liang, Hamzeh Alizadeh et al.",
  "category": "research",
  "topics": "safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-01T03:46:25.000Z",
  "fetched_at": "2026-07-14T16:30:09.961Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3310",
  "original_url": "https://arxiv.org/abs/2606.01640v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}