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arXiv · 2512.15397

ORACLE: Time-Dependent Recursive Summary Graphs for Foresight on News Data Using LLMs

Lev Kharlashkin·Eiaki Morooka·Yehor Tereshchenko·Mika Hämäläinen
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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.

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Lev Kharlashkin, Eiaki Morooka, Yehor Tereshchenko, Mika Hämäläinen. 2025-12-17. ORACLE: Time-Dependent Recursive Summary Graphs for Foresight on News Data Using LLMs. https://arxiv.org/abs/2512.15397

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