Yinhong Liu

The current RL checkpoint outperforms larger LLMs in redesigning training environments, revealing that iterative learning enhances diagnostic capabilities.

A single pair of boundary tokens transforms hidden-state reasoning into a trainable and interpretable framework, revealing causal insights that were previously obscured.

Operadic consistency reveals a powerful new way to diagnose reasoning failures in LLMs, achieving correlations with accuracy that exceed traditional confidence metrics.