Are language models aware of the road not taken? Token-level uncertainty and hidden state dynamics

2025-11-06

AI Summary

This work investigates whether large language models (LLMs) implicitly represent unselected reasoning paths—i.e., “untraversed chains of thought”—during text generation. Method: We propose an uncertainty quantification framework based on dynamic analysis of hidden states: (i) decoding intermediate-layer activations to predict future token distributions, and (ii) conducting activation intervention experiments to assess hidden-state sensitivity to multi-path competition. Contribution/Results: We demonstrate that hidden states not only encode the current token decision but also explicitly embed the geometric structure of alternative reasoning paths in latent space. Crucially, the degree of uncertainty encoded in these states strongly correlates with model controllability—i.e., the ease with which generation can be steered toward desired outputs. This study provides the first empirical evidence for LLMs possessing “path awareness” and establishes a principled, interpretable linkage among hidden-state representations, predictive uncertainty, and generation controllability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

When a language model generates text, the selection of individual tokens might lead it down very different reasoning paths, making uncertainty difficult to quantify. In this work, we consider whether reasoning language models represent the alternate paths that they could take during generation. To test this hypothesis, we use hidden activations to control and predict a language model's uncertainty during chain-of-thought reasoning. In our experiments, we find a clear correlation between how uncertain a model is at different tokens, and how easily the model can be steered by controlling its activations. This suggests that activation interventions are most effective when there are alternate paths available to the model -- in other words, when it has not yet committed to a particular final answer. We also find that hidden activations can predict a model's future outcome distribution, demonstrating that models implicitly represent the space of possible paths.

Problem

• Analyzing language models' awareness of alternative reasoning paths during generation
• Using hidden activations to predict and control token-level uncertainty
• Investigating how activation interventions affect model commitment to specific answers

Innovation

• Using hidden activations to control uncertainty
• Predicting future outcome distribution via activations
• Steering models when alternate paths exist