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

Ensuring that reasoning traces are consistently faithful remains an open challenge.

— Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework  (2609.20973 - Cai et al., 17 Sep 2026) in Section 5.1, Checkably faithful trace interfaces

This leaves an unresolved tension: how to convey that certain reasoning steps influence answers without implying that their text can be read as meaningful.

— Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning  (2609.04194 - Du et al., 3 Sep 2026) in Conclusion

The research gap is not exposing reasoning traces, which is done, but establishing whether exposed traces can be trusted as evidence, which is open.

— Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance  (2609.10105 - Ansari, 9 Sep 2026) in Section 7.2, Implications for research

It is, however, unclear if the rationales the models give are faithful: They might just be post-hoc rationalizations.

— Deception by Omission: Language Models Knowingly Hide Their Mistakes  (2610.11351 - Florin et al., 8 Oct 2026) in Section 3.2, paragraph “Rationalization for deception by omission”

Absent ground truth, how to measure CoT faithfulness remains unsettled.

— The Reliance Hypothesis: Chain-of-Thought Faithfulness Rises as Language Resource Level Falls  (2610.22841 - Resck et al., 16 Oct 2026) in Section 2, Related Work, paragraph “Measuring CoT Faithfulness”

We cannot rule out, however, that the invalid generations would have behaved differently.

— The Reliance Hypothesis: Chain-of-Thought Faithfulness Rises as Language Resource Level Falls  (2610.22841 - Resck et al., 16 Oct 2026) in Limitations, paragraph “Valid generations”