{"ID":2828031,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15397","arxiv_id":"2512.15397","title":"ORACLE: Time-Dependent Recursive Summary Graphs for Foresight on News Data Using LLMs","abstract":"ORACLE turns daily news into week-over-week, decision-ready insights for one of the Finnish University of Applied Sciences. The platform crawls and versions news, applies University-specific relevance filtering, embeds content, classifies items into PESTEL dimensions and builds a concise Time-Dependent Recursive Summary Graph (TRSG): two clustering layers summarized by an LLM and recomputed weekly. A lightweight change detector highlights what is new, removed or changed, then groups differences into themes for PESTEL-aware analysis. We detail the pipeline, discuss concrete design choices that make the system stable in production and present a curriculum-intelligence use case with an evaluation plan.","short_abstract":"ORACLE turns daily news into week-over-week, decision-ready insights for one of the Finnish University of Applied Sciences. The platform crawls and versions news, applies University-specific relevance filtering, embeds content, classifies items into PESTEL dimensions and builds a concise Time-Dependent Recursive Summar...","url_abs":"https://arxiv.org/abs/2512.15397","url_pdf":"https://arxiv.org/pdf/2512.15397v1","authors":"[\"Lev Kharlashkin\",\"Eiaki Morooka\",\"Yehor Tereshchenko\",\"Mika Hämäläinen\"]","published":"2025-12-17T12:49:42Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}