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This work finds that simple difference of means vectors coherently represent reward hacking in Kimi K3, GLM 5.2, and Qwen 3.8 Max, and provides evidence that simple, white-box methods can be used to scalably study and monitor reward hacking behaviors in frontier open source models.
Puzzling internal representations as a practical tool for calibrating, auditing, and triaging language model forecasters and reasoning models more broadly is established.
This work investigates how MLLMs learn novel concepts by introducing concepts in unimodal pre-training or multimodal fine-tuning, and evaluates the model’s ability to generalize between the two settings, and evaluates how deeply a model maps concepts across modalities.
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