works on

From the 1 of 5 linked papers with an AI index.

collaborators

5 papers

cs.LG2026

The Computational Basis of Confidence in Large Language Models

Dharshan Kumaran, Viorica Patraucean, Maks Ovsjanikov +3

The paper investigates what the confidence signal in large language models actually represents, showing that answer logits often act as monotonic readouts of a latent decision vari…

q-bio.NC2026

Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist

Akshay K. Jagadish, Younes Strittmatter, Nori Jacoby +5

Across the sciences, autonomous systems are increasingly being used in closed-loop discovery, proposing new theories and designing and running experiments to test them. This approa…

cs.LG2026

ATLAS: Active Theory Learning for Automated Science

Noémi Éltető, Nathaniel D. Daw, Kimberly L. Stachenfeld +1

Advancing scientific understanding through mechanistic modeling requires posing the right experimental questions to yield maximally informative data. To automate this pursuit withi…

cs.LG2026

Causal Evidence that Language Models use Confidence to Drive Behavior

Dharshan Kumaran, Nathaniel Daw, Simon Osindero +2

Metacognition -- assessing the quality of one's own cognitive performance -- guides adaptive behavior across species. Substantial research demonstrates that confidence signals can…

cs.LG2026

How LLMs Detect and Correct Their Own Errors: The Role of Internal Confidence Signals

Dharshan Kumaran, Viorica Patraucean, Simon Osindero +2

Large language models can detect their own errors and sometimes correct them without external feedback, but the underlying mechanisms remain unknown. We investigate this through th…