Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics

Hugging Face Daily Papers Papers
chain-of-thought reasoning-models safety-monitoring hidden-representations probe-trajectory temporal-dynamics

Summary

This paper introduces a method for monitoring the reasoning process of Large Reasoning Models by analyzing probe trajectories—the evolution of a concept's probability across generated tokens. The approach uses temporal and signal-processing features from hidden representations to better predict future model behavior, achieving up to 95% AUROC with max-pooling.

Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain of Thought (CoT) reasoning. However, CoT is not always faithful to the model's final output, undermining its reliability as a monitoring tool. To address this, we investigate the hidden representations of LRMs to determine whether future behavior can be predicted from prompt and CoT representations. By evaluating a probe at each generated token, we construct a probe trajectory, the continuous evolution of a concept's probability across the reasoning process. We find that future model behavior is more distinguishable when examined over the full trajectory than from a single static prediction. To characterize these temporal dynamics, we extract signal-processing features that capture volatility, trend, and steady-state behavior, significantly improving the separation of future model states. We also present two methodological insights. First, template-based training data achieves near-parity with dynamically generated model responses, eliminating the need for a costly initial inference and labeling. Second, the choice of pooling operation is critical: average-pooling and last-token methods collapse to near-random performance, while max-pooling achieves up to 95% AUROC and yields stable probe trajectories. Using four datasets and four reasoning models across the domains of safety and mathematics, we demonstrate that trajectory features encode task-specific dynamics that improve outcome separability. These findings establish probe trajectories as a complementary framework for monitoring LRM behavior. Warning: This article contains potentially harmful content.
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Source: https://huggingface.co/papers/2605.18549

Abstract

Chain of Thought reasoning in Large Reasoning Models shows improved safety monitoring through temporal analysis of hidden representations, where probe trajectories and signal-processing features enhance prediction of future model behavior compared to static approaches.

Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through theirChain of Thought(CoT) reasoning. However, CoT is not always faithful to the model’s final output, undermining its reliability as a monitoring tool. To address this, we investigate thehidden representationsof LRMs to determine whether future behavior can be predicted from prompt and CoT representations. By evaluating a probe at each generated token, we construct aprobe trajectory, the continuous evolution of a concept’s probability across the reasoning process. We find that future model behavior is more distinguishable when examined over the full trajectory than from a single static prediction. To characterize thesetemporal dynamics, we extractsignal-processing featuresthat capture volatility, trend, and steady-state behavior, significantly improving the separation of future model states. We also present two methodological insights. First, template-based training data achieves near-parity with dynamically generated model responses, eliminating the need for a costly initial inference and labeling. Second, the choice ofpooling operationis critical:average-poolingand last-token methods collapse to near-random performance, whilemax-poolingachieves up to 95%AUROCand yields stable probe trajectories. Using four datasets and four reasoning models across the domains of safety and mathematics, we demonstrate thattrajectory featuresencode task-specific dynamics that improve outcome separability. These findings establish probe trajectories as a complementary framework for monitoring LRM behavior. Warning: This article contains potentially harmful content.

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