What does this equation mean?

Whether interpretability evidence becomes admissible for a safety certification is not a third axis; it is what the other two jointly produce, and the joint requirement is a conjunction rather than an average. Write A(t) for the share of a frontier model’s decision-relevant behaviour with a validated, causally checked account — Circuit Tracing’s own figures are the best public anchor for where A(t) sits today [ 2 ] — and S(t) for the share of published interpretability results built on a method that has cleared an agreed, cross-lab benchmark rather than a proxy metric of the kind SAEBench found unreliable [ 10 ] . A regulator or a court asked to accept mechanistic evidence needs both a guarantee about how much of the model the evidence covers and a guarantee that the method itself is not still under live dispute; a high-coverage result from a disputed method and a well-validated result covering a hand-picked sliver of the model fail for different reasons. Admissibility is therefore better modelled as a conjunction of two thresholds than a weighted sum of two moving averages: $R(t) = \mathbb{1}\!\left[A(t) \ge a^{*}\right] \cdot \mathbb{1}\!\left[S(t) \ge s^{*}\right]$. R(t) stays at zero however high either term climbs alone, exactly as the AISI account above already anticipates by asking for outside validation before treating interpretability-based detection as sufficient on its own [ 11 ] . Axis A determines whether A(t) can plausibly clear $a^{*}$ within the horizon this article considers; Axis B determines whether S(t) can. Neither can be inferred from the other, which is why they are kept as two axes rather than folded into one.