{
  "id": 6471,
  "url": "https://arxiv.org/abs/2604.01588v1",
  "title": "NED-Tree: Bridging the Semantic Gap with Nonlinear Element Decomposition Tree for LLM Nonlinear Optimization Modeling",
  "summary": "Automating the translation of Operations Research (OR) problems from natural language to executable models is a critical challenge. While Large Language Models (LLMs) have shown promise in linear tasks, they suffer from severe performance degradation in real-world nonlinear scenarios due to semantic misalignment between mathematical formulations and solver codes, as well as unstable information extraction. In this study, we introduce NED-Tree, a systematic framework designed to bridge the semant",
  "authors": "Zhijing Hu, Yufan Deng, Haoyang Liu, Changjun Fan",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-02T03:59:37.000Z",
  "fetched_at": "2026-07-14T16:32:33.098Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6471",
  "original_url": "https://arxiv.org/abs/2604.01588v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}