AwarenessBench: Assessing Cognitive Capabilities of Language Models

Xiaojian Li; Rongwu Xu; Tianyun Zhang; Yue Wang; Shuo Chen; Qiner Lyu; Briana Zhang; Peiran Yang; Kyle Xue Chen; Haoyuan Shi; Yu Wang; Wei Xu

arXiv · 2609.35409

AwarenessBench: Assessing Cognitive Capabilities of Language Models

Xiaojian Li·Rongwu Xu·Tianyun Zhang·Yue Wang·Shuo Chen·Qiner Lyu·Briana Zhang·Peiran Yang·Kyle Xue Chen·Haoyuan Shi·Yu Wang·Wei Xu

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Abstract

As language models (LMs) exhibit increasingly consciousness-like behaviors, evaluating their cognitive abilities becomes essential. We introduce AwarenessBench, the first comprehensive benchmark for assessing the cognitive abilities of LMs in four dimensions: metacognition, self-awareness, social awareness, and situational awareness, covering 15 cognitive functions and 14,381 samples. Evaluating 18 state-of-the-art LMs, we find that all consistently surpass random baselines, with more advanced models performing better. We further compare LMs with human performance across three demographic groups, where the best-performing model surpasses human averages overall, but most still fall markedly short in metacognition and self-awareness. Finally, we show that awareness is a distinct capability: progress in language modeling or reasoning does not necessarily translate into improved cognition.

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Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu. 2026-09-28. AwarenessBench: Assessing Cognitive Capabilities of Language Models. https://arxiv.org/abs/2609.35409

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