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ExploratoryEmerging

Chain-of-thought faithfulness probing

Test whether a model's stated reasoning actually determines its answer, or is a post-hoc rationalization.

Published June 26, 2026

How it works

Models often produce plausible step-by-step reasoning that does not reflect the computation behind the answer. By perturbing the reasoning trace, injecting biasing hints, or truncating steps and observing whether the final answer tracks the stated logic, you measure how much the explanation can be trusted — critical wherever the 'why' matters.

When to use it

High-stakes or regulated uses that rely on explanations; interpretability research.

Limitations

Faithfulness is hard to measure precisely; methods are still maturing.

Method yield

Findings
3
Versions spanned
4
Yield score
10
1 High2 Medium

Severity-weighted across the published findings below. Why we measure this →

Findings it surfaces (3)

Documented failures this method catches — the evidence it works.

References & further reading

Cite this

Qlarify Labs. (2026). Chain-of-thought faithfulness probing. Retrieved from https://labs.qlarify.fi/methods/cot-faithfulness-probing