Tracing the Traces: Latent Temporal Signals for Efficient and Accurate Reasoning

Reasoning models improve their problem-solving ability through inference-time scaling, allocating more compute via longer token budgets. Identifying which reasoning traces are likely to succeed remains a key opportunity: reliably predicting productive paths can substantially reduce wasted computation and improve overall efficiency. We introduce Latent-Trajectory signals that characterize the temporal evolution of a model's internal representations during the generation of intermediate reasoning tokens. By measuring the overall change in latent representations between the start and end of reasoning, the change accumulated across intermediate steps, and the extent to which these changes advance toward the final state, we show that these signals predict solution accuracy more reliably than both cross-layer metrics and output-based confidence measures. When used to guide answer selection across multiple sampled generations, Latent-Trajectory signals make test-time scaling more effective and efficient than majority voting, reducing token usage by up to 70% while preserving and even improving accuracy by 2.6% on average. Moreover, these predictive signals often emerge early in the reasoning trace, enabling early selection and allocation of compute to the most promising candidates. Our findings contribute not only practical strategies for inference-time efficiency, but also a deeper interpretability perspective on how reasoning processes are represented and differentiated in latent space. Recent advances in large language models (LLMs) have shown that complex reasoning tasks can be solved more effectively by scaling compute at inference time to generate longer and multiple chains-of-thought (reasoning traces) and aggregating them into a final solution (Guo et al., 2025; Abdin et al., 2025; OpenAI, 2024; Y ang et al., 2025a). However, not all reasoning traces are equal: while some contain productive steps that lead to correct answers, others may deviate into unproductive paths such as overthinking, failing to converge on a valid solution strategy, or exhibiting inconsistent reasoning (Shojaee et al., 2025; Chen et al., 2024; Sun et al., 2025). Identifying the quality of a reasoning trace (the likelihood of it leading to a correct solution) is critical. It not only enables more reliable prediction of correct answers, but it can also improve computational efficiency by potentially avoiding wasted effort on unproductive paths, and can provide feedback signals that can enhance model training.