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cs.LG2026
Scientific Theory of a Black-Box: A Life Cycle-Scale XAI Framework Based on Constructive Empiricism
Sebastian Müller, Vanessa Toborek, Eike Stadtländer +3
Explainable AI (XAI) offers a growing number of algorithms that aim to answer specific questions about black-box models. What is missing is a principled way to consolidate explanat…
cs.LG2026
Improving Compactness and Reducing Ambiguity of CFIRE Rule-Based Explanations
Sebastian Müller, Tobias Schneider, Ruben Kemna +1
Models trained on tabular data are widely used in sensitive domains, increasing the demand for explanation methods to meet transparency needs. CFIRE is a recent algorithm in this d…
cs.LG2025
INTELLECT-3: Technical Report
Prime Intellect Team, Mika Senghaas, Fares Obeid +20
We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-…