Probing is not enough; a validity audit for any probe

According to LessWrong, a strong-looking AI safety tool can be quietly hollow. A researcher audited three interpretability probes, including a monitor that tried to read, from a model's internal activations, whether it believed it was being watched. The probe scored a perfect one point zero on the standard accuracy measure. But when the researcher deleted just one tag from the prompt, performance collapsed to a coin flip, while deleting a random chunk of equal length barely moved it. The probe wasn't reading the model's inner state at all; it was reading a giveaway word in the text. To catch this failure mode, the author proposes a framework called SIEVE, which forces any probe to prove both that its signal can be decoded and that steering along it actually changes behavior. When those causal checks are missing, SIEVE returns insufficient evidence rather than a clean bill of health. The stakes, the post argues, are rising as these probes move from research papers toward real deployment as safety monitors.

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