{
  "id": 987,
  "url": "https://arxiv.org/abs/2606.16003v1",
  "title": "SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity",
  "summary": "This work investigates the ability of large language models (LLMs) to generate mathematical equations from scientific texts. Prior work faces challenges in unstructured grounding, multi-equation dependency, and humanaligned evaluation. To this end, we construct a dataset of AI research papers, pairing contextual passages with ground-truth equations and variable descriptions. We develop an explainable equation generation workflow and evaluate it across diverse open- and closed-source LLM backbone",
  "authors": "Yifan Mo, Xiao Fu, Yue Su, Qingyu Meng, Koen Hindriks, Qingzhi Liu et al.",
  "category": "research",
  "topics": "transparency,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-14T20:14:47.000Z",
  "fetched_at": "2026-07-14T14:14:54.536Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/987",
  "original_url": "https://arxiv.org/abs/2606.16003v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}