The consequence is a measurement instruction rather than a theorem: a transfer conclusion read at one strength does not identify what changed, because a displacement and a gain are not distinguishable from a single operating point.
Abstract
Additive activation steering is calibrated in single-turn chat, then deployed inside agent scaffolds. The quantity usually reported for that move is a gain: a ratio of steered effects, T = Delta_agent / Delta_chat. We sweep eight family x arm dose-response cells over six models in both deployment contexts and show this ratio does not identify potency and efficacy. Reconstructing the published estimator in both of its forms on our own grids, its realized range contains 1 in five of five scorable cells, it moves with dose in four of five, and two cells with opposite potency shifts, both CI-clean on the primary grid, return gain intervals overlapping at a width under 0.08. Every scorable cell is an amplifier at one dose and an attenuator at another, so an amplify/attenuate taxonomy reports the dose it was read at. We replace the gain with a location: dEC50 = EC50_agent - EC50_chat, the cross-context difference in a curve location. It is signed both ways on this roster's primary grid, across four model families (+1.013 [+0.777, +1.273] against -12.368, -10.855, -5.497 and -886.066 elsewhere) and beats a vertical rescaling at equal complexity in all five cells of a frozen audit (four under the registered trim). Three deflationary accounts are measured and rejected on sign pattern and magnitude. We report the discipline at the same volume as the result: one cell is quarantined loudly, our pre-registered forecaster was refuted out of sample and is published as refuted, a registered salvage claim produced no qualifying cell and is reported unanswered, and a census of our own register reports registered branches no code here could have emitted. The consequence is a measurement instruction rather than a theorem: a transfer conclusion read at one strength does not identify what changed, because a displacement and a gain are not distinguishable from a single operating point.
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A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.