Researchers propose a method called PonderTTT for adaptive compute allocation in large language models. This approach uses self-supervised reconstruction loss from the TTT layer to selectively trigger updates, reducing unnecessary computation. The method is training-free and requires only a single scalar threshold calibrated on unlabeled data. Experiments with GPT-2 models show that this approach achieves high oracle recovery rates while outperforming random skip baselines. --- Why it matters: This work matters because it addresses the inefficiency of large language models applying uniform computation to all inputs, regardless of difficulty. By adapting compute allocation based on input complexity, PonderTTT can reduce computational costs and improve model performance. Source: https://arxiv.org/abs/2601.00894