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arXiv · 2610.16322

BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction

Tobias Schneider·Danial Fahrner·Soren Antebi·Daniel Uedelhoven·Lennard Bodden·Sandra Halscheidt·Rafet Sifa
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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.

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Tobias Schneider, Danial Fahrner, Soren Antebi, Daniel Uedelhoven, Lennard Bodden, Sandra Halscheidt, Rafet Sifa. 2026-10-12. BehaviorFormer: Discovering Behavioral Patterns via Clusterable Next-Token Prediction. https://arxiv.org/abs/2610.16322

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