Are Latent Reasoning Models Easily Interpretable?
Summary
The paper investigates latent reasoning models (LRMs) and their interpretability. It finds that latent reasoning tokens are often unnecessary for predictions, suggests LRMs may still be interpretable in many cases, and shows methods to decode verified reasoning traces for correct predictions, with implications for evaluating and trusting AI reasoning processes.