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arXiv · 2509.13333

Evaluation Awareness Scales Predictably in Open-Weights Large Language Models

Maheep Chaudhary·Ian Su·Nikhil Hooda·Nishith Shankar·Julia Tan·Kevin Zhu·Ryan Lagasse·Vasu Sharma·Ashwinee Panda
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Abstract

Large language models (LLMs) can internally distinguish between evaluation and deployment contexts, a behaviour known as \emph{evaluation awareness}. This undermines AI safety evaluations, as models may conceal dangerous capabilities during testing. Prior work demonstrated this in a single $70$B model, but the scaling relationship across model sizes remains unknown. We investigate evaluation awareness across $15$ models scaling from $0.27$B to $70$B parameters from four families using linear probing on steering vector activations. Our results reveal a clear power-law scaling: evaluation awareness increases predictably with model size. This scaling law enables forecasting deceptive behavior in future larger models and guides the design of scale-aware evaluation strategies for AI safety. A link to the implementation of this paper can be found at https://anonymous.4open.science/r/evaluation-awareness-scaling-laws/README.md.

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Maheep Chaudhary, Ian Su, Nikhil Hooda, Nishith Shankar, Julia Tan, Kevin Zhu, Ryan Lagasse, Vasu Sharma, Ashwinee Panda. 2025-11-09. Evaluation Awareness Scales Predictably in Open-Weights Large Language Models. https://arxiv.org/abs/2509.13333

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