{
  "id": 7066,
  "url": "https://arxiv.org/abs/2603.17694v1",
  "title": "MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment",
  "summary": "In the real economy, modern decision-making is fundamentally challenged by high-dimensional, multimodal environments, which are further complicated by agent heterogeneity and combinatorial data sparsity. This paper introduces a Multi-Agent Large Language Model-based Economic Sandbox (MALLES), leveraging the inherent generalization capabilities of large-sacle models to establish a unified simulation framework applicable to cross-domain and cross-category scenarios. Central to our approach is a pr",
  "authors": "Yusen Wu, Yiran Liu, Xiaotie Deng",
  "category": "research",
  "topics": "safety-alignment,jobs-economy,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-18T13:11:09.000Z",
  "fetched_at": "2026-07-14T16:32:59.162Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7066",
  "original_url": "https://arxiv.org/abs/2603.17694v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}