{
  "id": 573,
  "url": "https://arxiv.org/abs/2606.26787v1",
  "title": "AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing",
  "summary": "Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement. We propose AIGP, a novel framework that leverages a Large Language Model (LLM) prompted with domain knowledge, structured data and textual context to make interpretable, knowledge-aware pricing decis",
  "authors": "Chennan Ma, Yanning Zhang, Siqi Hong, Xiuchong Wang, Fei Xiao, Keping Yang",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-25T09:21:42.000Z",
  "fetched_at": "2026-07-14T14:14:37.249Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/573",
  "original_url": "https://arxiv.org/abs/2606.26787v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}