When a model changed, its open recipe helped trace why.

The human problem

When post-training changes model behavior, developers may see the regression without being able to inspect how it formed.

The prior constraint

Closed data, code, and checkpoints make causal debugging of model behavior difficult.

AI’s actual role

Interpretability tools examined internal and behavioral changes across an openly documented post-training process.

The documented result

The 9 September case study reports tracing a known regression using OLMo's available stack. It does not establish detection of unknown problems in general.

Why it may matter

Openness can support investigation after a benchmark moves, not only reuse of final weights.

Limitations

This is a case study from participating organizations.

It begins with a known regression.

The method may not transfer to closed models or every failure mode.

Unresolved questions

Can it discover unanticipated regressions?

Which artifacts are essential for a reproducible explanation?

How should competing causal interpretations be tested?