Title: Can We Predict Alignment Before Models Finish Thinking? Towards Monitoring Misaligned Reasoning Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: reasoning language models, safety, safety alignment, chain of thought reasoning

Abstract: Reasoning language models improve performance on complex tasks by generating long chains of thought (CoTs), but this process can also increase harmful outputs in adversarial settings. In this work, we ask whether the long CoTs can be leveraged for predictive safety monitoring: *do the reasoning traces provide early signals of final response alignment that could enable timely intervention?* We evaluate a range of monitoring methods using either CoT text or activations, including highly capable large language models, fine-tuned classifiers, and humans. First, we find that a simple linear probe trained on CoT activations significantly outperforms all text-based baselines in predicting whether a final response is safe or unsafe, with an average absolute increase of $13$ in F1 scores over the best-performing alternatives. CoT texts are often unfaithful and misleading, while model latents provide a more reliable predictive signal. Second, the probe can be applied to early CoT segments before the response is generated, showing that alignment signals appear before reasoning completes. Error analysis reveals that the performance gap between text classifiers and the linear probe largely stems from a subset of responses we call *performative* CoTs, where the reasoning consistently contradicts the final response as the CoT progresses. Our findings generalize across model sizes, families, and safety benchmarks, suggesting that lightweight probes could enable real-time safety monitoring and early intervention during generation.

TL;DR: We show that a simple linear probe on CoT activations of misaligned reasoning models can detect unsafe responses earlier and more reliably than text-based methods.