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A steering knob can make a language model express a trait more
strongly. But what does a setting of one actually mean?

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The same internal strength can produce different behavior across
traits and models.

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PersonaDose asks for something more useful: choose a trait,

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then request a measured average intensity.

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We will use the paper's sycophancy experiment, which measures a
model's tendency to agree with the user.

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A request of sixty means a target mean on the study's behavioral
rubric, not sixty percent of answers.

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The challenge has two parts: learn a useful behavioral range,
then calibrate access to it.

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The base language model stays frozen. A learned controller
transforms its hidden activations during generation.

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A description tells the controller which trait to express; flow
time determines the intervention strength.

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Flow time is a controller parameter, not the time the viewer
waits for an answer.

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Within one base model, the seven evaluated traits share the same
controller.

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Different base models have separate controllers.

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The controller learns from trait-expressing responses, without
pairing each response with a requested intensity label.

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That separation matters: response training supplies the
behavior,

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while calibration later assigns meaningful score units.

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First, the authors try a grid of controller settings on
calibration questions and measure mean trait expression.

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A monotone fitted curve summarizes how measured expression
changes with strength,

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with interpolation between sampled settings.

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To serve a target score, the method works backward through that
curve to select an internal setting.

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If several settings share a fitted score, it chooses the
smallest strength on that plateau.

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A target outside the fitted range is marked unreachable before
testing.

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A reachable setting is fixed for held-out questions.

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This avoids selecting the best-looking setting after seeing the
test responses. The mapping is established beforehand.

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The study examines seven traits in Qwen. Here is one calibrated
request: sycophancy at sixty.

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The fitted map selects flow time one point four eight six four
for the initial controller.

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With that setting fixed, held-out responses average fifty-six
point two five on the trait scale.

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The table reports an absolute error of three point seven five.
This describes a measured average, not every answer.

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Each plotted cell averages ten held-out questions, with ten
sampled responses per question.

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A calibrated request therefore connects an internal intervention
to a behavioral measurement

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that can be checked on new questions.

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The same Qwen controller serves four requested intensities:
twenty, forty, sixty, and eighty.

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Their held-out means are twelve point nine two, thirty-seven
point one three,

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fifty-six point two five, and eighty point five.

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Only the selected steering setting changes. These requests use
the same controller weights.

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Mean coherence in this example ranges from eighty-eight point
nine two to ninety-two point nine one.

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Coherence measures intelligibility under the rubric.

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It is separate from how closely the requested trait score is
reached.

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The pattern illustrates graded access, while the gaps between
requested and realized values remain visible.

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Across three models, the study tests seven trained traits and
four targets,

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giving twenty-eight possible requests per model.

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Llama reaches twenty-two requests, Qwen twenty,

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and Gemma fourteen within their calibration ranges.

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Mean targeting errors over those reachable requests are six
point one, six point two, and four point seven.

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Gemma's smaller error accompanies a narrower supported range.

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The error alone cannot tell you how many requests are available.

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The requests also meeting the held-out coherence floor number
nineteen, twenty, and thirteen, respectively.

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Useful dosing needs these three checks together: reachability,
targeting error, and coherent expression.

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The map belongs to a specific controller. Continued training can
change the relationship between flow time and behavior.

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In the paper's separate Qwen follow-up, the base language model
still stays frozen while the controller is updated.

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For sycophancy at sixty, the initial map selects one point four
eight six four;

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the continued controller's map selects zero point eight seven
four five.

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The same behavioral request therefore uses a different internal
strength after the update.

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The authors remeasure the curve before serving requests with the
updated controller.

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Calibration makes the interface interpretable, but it must
follow the system whose behavior it measures.

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PersonaDose separates learning a trait from specifying how much
of it to express.

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A shared controller provides the behavior. A measured map
translates requested score units into its internal strength.

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The Qwen example makes the contract concrete: request sixty,
choose the calibrated setting,

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then measure fifty-six point two five on held-out responses.

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That is control you can inspect through its range, its targeting
error, and its coherence.

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The study concerns judged mean behavior on trained traits and
short responses; individual answers can still differ.

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A meaningful steering interface starts when an internal knob
becomes a measurable behavioral request.