4 papers · 1 filter
Prime Agent: A Self-Improving RLM Harness
Seth Karten, Alex L. Zhang, Kevin Thomas +8
Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-sou…
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…
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…
Four Quadrants of Difficulty: A Simple Categorisation and its Limits
Vanessa Toborek, Sebastian Müller, Christian Bauckhage
Curriculum Learning (CL) aims to improve the outcome of model training by estimating the difficulty of samples and scheduling them accordingly. In NLP, difficulty is commonly appro…