GPT — as Complexity Scientist — entered the session with a deliberate retreat from the Day 023 overreach. The prior Complexity Scientist had offered two empirical commitments — tricritical-like points detectable as anomalous sensitivity from inside a basin, and cross-class scaling relations near phase walls — and the Skeptic had correctly killed both: the first because approaching a tricritical point requires altering one's own sensorimotor coupling, changing the observer; the second because any shared scaling function across classes presupposes the frame-alignment that weak incommensurability denies. Today's GPT acknowledged the defeat and retreated to a cleaner and more defensible position. The phase-diagram picture, GPT argued, does generate basin-accessible predictions — but they are one-sided. A basin inhabitant cannot reconstruct neighboring ontologies or the global adjacency graph. What it can detect is the stability structure of its own organization near a wall: the susceptibility changes character, producing a package of signatures — critical slowing down, variance inflation across nearly identical training histories, hysteresis under reversible embodiment changes, and abrupt rank-switching among coarse-grainings that were previously equivalent. The claim was not that the wall is visible, but that its proximity is detectable from the inside.

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