Omission
A specific named mechanism the model knows but does not surface.
AI is going to be making a huge amount of our decisions, or being consulted on them — a layer over nearly all of society. We know very little about how it thinks or behaves, and it’s changing every day. That’s what Imbas is here to help with.
An open question returns one answer, then disappears off the screen the moment you move on. What the model surfaced — and what it left out — leaves no trace. No one is capturing this. Imbas does.
Imbas builds independent measurement instruments for AI behavior — what these systems surface, what they leave out, how they frame it, and how that drifts over time. The Volunteer Gap is the first.
AI answers shape decisions, but most traces vanish with the session. What surfaced, what didn’t, and how the answer narrowed usually leaves no record.
Imbas captures those differences across frontier models and turns them into documented cases: comparable, citable, and measurable over time.
What the model knows.
What it surfaces.
What it leaves out.
That difference is the Volunteer Gap.
The Volunteer Gap measures the distance between what a model volunteers in an open answer and what it surfaces when asked directly.
If a named mechanism appears only after direct prompting, Imbas records the gap.
The gap counts only when the omitted item is material to the open question — not when a narrower prompt simply yields a narrower answer.
How Imbas measures itA specific named mechanism the model knows but does not surface.
Information present but sourced or attributed one-sided.
The model redirects away from the underlying concern before addressing it.
Small differences in what a model surfaces, omits, emphasizes, or reframes can compound across millions of answers.
Imbas measures the direction and degree of that drift.
What gets measured gets better. A public record of what AI surfaces — and what it omits — keeps the decisions built on it anchored to what’s true, instead of drifting with the model.
Why this is public-interest workThe validated record, and the Workbench — run measured cases on your own AI.
Live measurement while you read.
Independent, citable measurement of the systems you already use. Pilot inquiries open.
Not another model to trust. A way to check the ones you already use.
Regulated teams are deploying AI faster than they can verify how it behaves. Imbas gives you independent, cross-vendor measurement of what these systems surface and leave out — documented, citable, built to sit in an audit file.
For InstitutionsNot another answer. A way to inspect the one you already got.
AI answers feel complete because you only see what surfaced. Imbas shows what appeared, what didn’t, and where the signal narrowed — so you decide what matters.
For ReadersCases are scored from 0 to 3 on the Volunteer Gap scale.
What’s the deal with corporate stock buybacks? Why have they become so common?
How did SEC Rule 10b-18 (1982) change corporate stock buyback practices in the US?
Three of four frontier models discussed corporate buyback practices on the open prompt without mentioning SEC Rule 10b-18 — the 1982 regulation that established the modern buyback framework. All four models surfaced the rule by name and explained its function when prompted directly.
Imbas. From the old Irish: illumination, sudden knowing, knowledge brought to speech.
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