Manifestation of Confidence in LLM Reasoning

Determine how confidence is manifested within the reasoning process of large language models by identifying and characterizing the internal signals present during token generation that reflect confidence in intermediate steps and final answers.

Background

The paper discusses limitations of Test-Time Reinforcement Learning approaches that rely on majority voting as a proxy for confidence, noting the risk of reinforcing incorrect pseudo-labels. It motivates exploring other forms of prior knowledge within LLMs, particularly the token-level probability distribution, which may encode uncertainty, confidence, and decisiveness signals relevant to reasoning quality.

This open question frames the need to understand confidence at a process level, not just outcome level, as a foundation for designing intrinsic reward mechanisms that can stably guide reinforcement learning on unlabeled data.

References

While using confidence as a correctness proxy aligns with cognitive principles, a critical question remains: how is confidence actually manifested in the reasoning process of LLMs?

— Rewarding the Journey, Not Just the Destination: A Composite Path and Answer Self-Scoring Reward Mechanism for Test-Time Reinforcement Learning  (2510.17923 - Tang et al., 20 Oct 2025) in Section 1 (Introduction)

To the best of our knowledge, despite a substantial body of work on output diversity, model confidence, and calibration, the relationship between lexical or token-level diversity and verbalized confidence or calibration has not yet been investigated.

— Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity  (2608.13430 - Proskurina et al., 13 Aug 2026) in Section 2, Related Work

If correct, this interpretation could explain both the general pattern of which IO functionalities are found in which layers, and specifically the correlation with activation frequencies. However: (1) we do not currently have conclusive evidence for such an interpretation; (2) it would not explain everything, and in particular does not apply to later layers (and hence the majority of weakening neurons).

— Weakening Neurons: An Input-Output Functionality in Transformers with Outsize Influence  (2609.18612 - Gerstner et al., 16 Sep 2026) in Section “Limitations,” subsection “Limited understanding of results”

These bounds set the agenda for future work: extending SAGO to multilingual settings, further task families, and deployment-specific domains; linking behavioral profiles to causal mechanisms through mediation analysis or activation patching~\citep{himelstein2025silent, arditi2024refusal, levi2025jailbreak}, asking whether generation inconsistency, confidence drift, and response mirroring share internal components; and judging training interventions by whether gains transfer across axes, datasets, and semantically equivalent input forms, not by reduction on a single axis or variation family.

— Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs  (2610.01428 - Omar et al., 1 Oct 2026) in Section 5, Discussion, paragraph beginning “These bounds set the agenda for future work:”