Temporal Predictors of Outcome in Reasoning Language Models

• URL: http://arxiv.org/abs/2511.14773v1
• Date: Mon, 03 Nov 2025 08:57:18 GMT
• Title: Temporal Predictors of Outcome in Reasoning Language Models
• Authors: Joey David,
• Abstract summary: Chain-of-thought (CoT) paradigm uses the elicitation of step-by-step rationales as a proxy for reasoning.<n>We show that, for harder questions, a drop in predictive accuracy highlights a selection artifact.<n>Overall, our results imply that for reasoning models, internal self-assessment of success tends to emerge after only a few tokens.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The chain-of-thought (CoT) paradigm uses the elicitation of step-by-step rationales as a proxy for reasoning, gradually refining the model's latent representation of a solution. However, it remains unclear just how early a Large Language Model (LLM) internally commits to an eventual outcome. We probe this by training linear classifiers on hidden states after the first t reasoning tokens, showing that eventual correctness is highly predictable after only a few tokens, even when longer outputs are needed to reach a definite answer. We show that, for harder questions, a drop in predictive accuracy highlights a selection artifact: hard items are disproportionately represented in long CoTs. Overall, our results imply that for reasoning models, internal self-assessment of success tends to emerge after only a few tokens, with implications for interpretability and for inference-time control.

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