Extent of Chain-of-Thought Faithfulness to Underlying Computation

Determine the degree to which chain-of-thought outputs generated by large language models faithfully represent the models' underlying computational processes that produce their final answers.

Background

The paper motivates REMuL by noting that chain-of-thought (CoT) explanations may not reflect how a LLM actually computes its answers. Prior studies have questioned whether CoT is faithful, raising concerns for verification and reliability in high-stakes domains.

REMuL is proposed as a training framework that incentivizes reasoning traces executable by other models, aiming to improve faithfulness without sacrificing accuracy. Despite these contributions, the authors explicitly acknowledge that the fundamental question of how faithfully CoT mirrors the true internal computation remains unresolved.

References

While current reasoning LLMs produce chain-of-thought (CoT) outputs that ostensibly reflect their reasoning, it remains unclear to what extent these outputs faithfully represent the model's true computational process \citep{lanham2023measuring, barez2025chain, chen2025reasoning}.

— Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution  (2602.16154 - Sivakumaran et al., 18 Feb 2026) in Section 1 (Introduction), first paragraph