ARTI: Detecting and Routing Reasoning Types from Embedding Geometry
Abstract
We show that different reasoning types leave distinct geometric signatures in transformer embedding spaces, detectable by a lightweight classifier with only 20K parameters. ARTI classifies 10 reasoning types at 84.2% accuracy (8.4x chance). ARTI-routed scoring achieves StrategyQA 81.0% (+32.6pp). Three-experiment falsification chain proves frozen encoder is structurally necessary. GCMC validation: R² = 0.678.
Fields of Study & Subjects
Topic Modeling
Explainable Artificial Intelligence (XAI)
Multimodal Machine Learning Applications
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 27 February 2026
- Language
- EN
- Document type
- Preprint