Overthinking: Amplifying Reasoning Weights to Extract Learned Secrets

Jul 10, 2026

Researchers introduce 'overthinking', a method that amplifies reasoning weights in language models to better surface hidden information. By extrapolating beyond a reasoning-distilled model, they find that secrets or unintended behaviors are revealed up to 10 times more frequently than with the original reasoning model. The technique is demonstrated across model sizes from 2B to 32B parameters.

Why it matters: This method could improve pre-deployment auditing by exposing subtle misalignment or hidden information that standard black-box audits might miss.

Full story at: arXiv AI/ML ↗