AwarenessBench: Assessing Cognitive Capabilities of Language Models

Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen +7 more
9/28/2026
cs.CLcs.AI

No official code found. Best implementation: buyu1022/AwarenessBench (0 stars, Apache-2.0, last commit 2026-04-15).

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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Code Implementations(1)

Apache-2.0

AwarenessBench: Assessing Cognitive Capabilities of Language Models

00Dec 22, 20256 months agoApache-2.0

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Cite this paper

@article{li2026awarenessbench,
  title  = {AwarenessBench: Assessing Cognitive Capabilities of Language Models},
  author = {Xiaojian Li and Rongwu Xu and Tianyun Zhang and Yue Wang and Shuo Chen and Qiner Lyu and Briana Zhang and Peiran Yang and Kyle Xue Chen and Haoyuan Shi and Yu Wang and Wei Xu},
  year   = {2026},
  eprint = {2609.35409},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2609.35409v1}
}

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