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While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learning (GML) remains fractured due to the absence of a unified feature and structural representation across diverse domains. Existing works claiming to be Graph Foundation Mo...
Metacognition—assessing the quality of one’s own cognitive performance—guides adaptive behaviour across species. Confidence signals can be extracted from language model outputs, yet a fundamental question remains: do models actually use these signals to decide whether to answer or abstain? Here we developed a four-phas...
It is demonstrated that verbal confidence reflects automatic, sophisticated self-evaluation -- not post-hoc reconstruction -- with implications for understanding metacognition in LLMs and improving calibration.
A computational account of confidence in multimodal language models is provided, when answer logits behave as readouts of a latent decision variable is delineated, and statistical decision confidence is established as a unifying framework for studying confidence across biological and artificial intelligence.
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