BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction
Tobias Schneider
Danial Fahrner
Soren Antebi
Daniel Uedelhoven
Lennard Bodden
Sandra Halscheidt
Rafet Sifa
Oct 2026
Artificial Intelligence
Machine Learning
Human-computer Interaction
Abstract
Discovering behavioral patterns in open-ended games is challenging due to the lack of clear objectives or ground-truth annotations. In this work, we extend a next-token prediction Transformer with the deep clustering algorithm DCSS to learn meaningful representations with well-separated clusters. In a controlled Minecraft session with 26 players, the method discovers 23 distinct behavioral patterns that generalize across the studied players, further refining structures present in self-supervised representations into clearly separable clusters. We group the clusters into seven interpretable categories covering stationary, roaming, travel, mining, base work, logging, and farming behavior.
