Reading Between the Dots: Decoding Hidden Computation across Filler Tokens
Researchers found that large language models perform hidden reasoning over filler tokens (like dots) to improve performance. They developed an unsupervised decoding pipeline to recover these intermediate values from the model's residual stream. This shows that behavioral monitoring is not the only way to audit models, as hidden computation is partially accessible through this method.