Plausible reasoning sounds right.

Faithful reasoning preserves the structure of the thing being reasoned about.

That distinction matters because many failures do not look like nonsense. They look coherent. They explain themselves well. They use the right vocabulary. They produce an answer that could have been true.

The problem is that “could have been true” is a weak standard.

In writing, plausibility shows up as an argument that flows but hides a missing step. In research, it shows up as a result that has a clean story but rests on a proxy. In AI systems, it shows up as an answer that sounds grounded while drifting away from the actual process that produced it.

Faithfulness asks a harder question:

Did this explanation, evaluation, or answer preserve the real causal path?

This is why I keep returning to the distinction in evaluation work. A benchmark can produce plausible evidence of capability while failing to measure the behavior we care about. A judge can produce plausible labels while tracking surface features. A model can produce plausible rationales while concealing the actual reason for its output.

The useful test is not whether the story is smooth.

The useful test is whether removing any step would break the claim.