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This work investigates how large language models acquire metacognitive monitoring, the ability to know what one knows, by training 10 open-weight LLMs to predict their own accuracy on factual multiple-choice questions before answering them and finds that trained confidence reflects two distinct signals.
This paper proposes the use of curiosity-driven exploration algorithms from artificial intelligence to systematically and efficiently cover the space of possible interference behaviors and shows that the proposed approach achieves broader and more uniform behavioral coverage within a limited experimental budget.
It is demonstrated that effective and generalizable transmission protocols can be discovered in silico, suggesting a path toward AI-assisted design of coordination infrastructure that enhances human collective intelligence.
A closed-loop framework based on autotelic reinforcement learning is introduced, in which an agent autonomously samples diverse goals and learns a goal-conditioned policy to intervene in a complex system through minimal, local perturbations, suggesting a path toward artificial experimentalist agents that, autonomously...
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