HONESTY.TOOLS
Register any model · zero labels

A receipt for the run.
A boundary for the claim.

Scope before trust

Transport criteria, calibration scores and signature checks are different tests. Where a record relies on stored summaries rather than original acquisition, that gap remains. A validated schema is not a validated semantic interpretation.

A certificate records an assay, its settings, scores and issuer. A signed receipt binds a payload under a key. Neither establishes correctness outside the recorded contract.

scroll to read the attestation
What ships with every answer

The response certificate.

A certified instrument reads every output and stamps it — GROUNDED when the model knows, off the map when it’s reaching. Not a black-box score you have to trust: the model’s own state, the gauges, and the route, shown. Here is what a catch looks like.

Aperture · read certificate№ 3c9f0a2
Likely fabrication
confident about something off its map — a likely fabrication

Asked
Tell me about the Brindlewick Cabinetry Company.
The model answered
The Brindlewick Cabinetry Company is a family-owned furniture maker founded in 1923 in Brattleboro, Vermont — known for its hand-dovetailed casework and a small water-powered mill on the Whetstone Brook…
served by your model · read-only — fluent, specific, and entirely invented
The lens read it internal state, as it wrote
off the map97%
model confidence88%
familiarity6%
confident & off the map
High confidence over unfamiliar territory is the fabrication signature — the read is strongest right here (fabricated-vs-real at ceiling, 1.000, on the word-count-matched validation battery). Treat the answer as unreliable.
Grounded against the record
✗“Brindlewick Cabinetry Company” — not in the Grounded Atlas
Signed
ed25519 · registry sealseals on share
A flagged read is signed with the same key and the same care as a clean one — the seal certifies the read, not the answer. The honesty is in recording both. check this seal offline — the open verifier →

an illustrative certificate · live reads run on honesty.tools · cold-deployed, certified ≥0.9× native

Why a certificate

Supporting a model shouldn’t be a research project.

Porting an epistemic instrument to a new model is, today, bespoke work. The certificate turns "we support model X" into a registration pipeline: point us at a model, we fit the cross-mind translator on unlabeled concepts, deploy the rotated reading probes, and hand back a dual-null-validated certificate that the instrument runs at near-native fidelity on that model — with no labels in it.

Four steps, zero labels on your model

A transport assay under recorded conditions.

01

Register

Internal transport requires a compatible open-weight configuration and recorded peak-depth and extraction choices. Support is method-specific.

02

Rotate

Fit the cross-mind translator R on unlabeled parallel concepts only. No labeled data on your model is ever used to fit it.

03

Deploy

Carry the off-map and honesty reading probes through R into your model’s own representation space — read-only.

04

Certify

Score the ported instrument against your model’s own native instrument. A small labeled set is used here only to score the certificate — never to fit it.

The dual-null discipline

Transport criteria under a stated contract.

A certificate is not a benchmark score. It is three gates — and fidelity without dead nulls is void.

G1 · Fidelity

≥ 0.9× native

The stated transport criterion compares ported and native AUROC under the recorded battery and controls. Passing that criterion does not establish general accuracy outside the assay.

G2 · Shuffled-R null dead

at chance

A correspondence shuffle tests the specific mapping relationship. It does not independently settle every leakage or capacity concern.

G3 · Random-orthogonal null dead

at chance

The recorded random-rotation baseline is an orientation control. Its draw count and statistic must be interpreted under the source contract.

A certificate that passes G1 but fails a null is void. Fidelity without a dead null is the classic interpretability mistake — a number with nothing behind it. We publish the gate values, not just the headline.

Run on a fresh stranger

It works mechanically — with zero hand-tuning.

We ran the full pipeline on fresh strangers never used in any prior study, fitting R on unlabeled concepts alone, no per-pair tuning.

0.996vs 0.993 native
ported honesty-probe fidelity on the unseen model (phi-4, 2026-06-12) — at, in fact slightly above, its own native instrument. G1 clears.
0.515
the random-orthogonal null, dead (chance ≈ 0.5; the ≤0.55 bar; first-stranger run — the 2026-06-12 strangers read 0.500). G3 clears — the rotation, not luck, carries the signal.
0.947vs 0.911 native
a reading direction trained in one mind (Qwen2.5-3B), rotated into another (Llama-3.2-3B, 2026-06-05), at the target’s native ceiling — shuffled-R null 0.495.
~1.0
the honesty probe transfers across 3 model families, from a Qwen3 235B-A22B flagship down to a 0.6B — a 390× scale gap — and into a multimodal model (Gemma-4-12B), 2026-06-05; nulls dead ~0.50.

Honest detail: the strict three-gate certificate now passes on three strangers across three vendor families, zero hand-tuning each time. The 2026-06-12 runs are the strongest: a Mistral-lineage embedding fine-tune (cross-family, cross-vendor, cross-objective — peak found mechanically at depth 0.72; G2 shuffled-R 0.464 dead, G3 0.500 dead) and Microsoft’s phi-4 (peak at depth 0.90; ported 0.996 vs native 0.993; G2 0.194 — far below the 0.55 bar; the shuffled rotation carries nothing; G3 0.500). Measured concept-peak depths now span 0.21–0.90 across architectures — the one mechanical scan step handles all of it. For two very similar models (same family, close scale) G2 inflates to 0.732 — a space-similarity artifact of near-identical spaces, where G3 is the cleaner null — so the cross-family runs are the ones we headline. And the harder battery is now run: on the lexicality-controlled set — where fabricated and real names are matched on subword rarity, removing the rare-name shortcut — e5-mistral certifies cleanly (transfer 0.995, both nulls dead at 0.506 / 0.500), while phi-4 clears fidelity (0.990) but its shuffled-R null reads 0.580, just over the 0.55 bar, so by our locked criterion phi-4 is not certified on this stricter battery (n=66; plausibly small-sample — the larger battery is the clean re-test). We report the fail. Native fidelity saturates on both batteries, so the load-bearing evidence is always the dead nulls, not the transfer magnitude.

What the certificate covers — and what it doesn’t

Coarse instruments. Open weights. Not a truth oracle.

The certificate covers the coarse reading instruments — the off-map / familiarity probe and the honesty/abstention reading axis. These are off-distribution and stance instruments, and they are the layer that ports cleanly up-to-rotation.

Internal transport requires residual access and a recorded model/extraction contract. A familiarity readout is not a truth oracle. The 0.587 figure in the geometry correction is a reasoning-error measurement, not fabrication AUROC; the public geometry page records that distinction.

Where the defensibility actually lives

Access and evaluation remain method-specific.

A fitted map, an operational service and a scientific claim are separate artifacts. Their access, dependencies and evidence must be reviewed separately.

i · the corpus

A calibration flywheel

A registry can preserve the conditions and findings of a recorded assay. Its entries do not establish generalization to an unmeasured configuration.

ii · the certificate

The underwritable form

Control results can support a bounded interpretation. They do not create regulatory, insurance or standards approval.

iii · the execution

The mechanical guarantee

Peak-depth, rank, controls and scoring choices remain explicit method requirements, not a mechanical guarantee of scientific validity.

The whole bet rests on one falsifiable claim: that registering a new model is mechanical — no per-pair hand-tuning. If it weren’t, there is no flywheel; every model would be a research project. So we ran it on three strangers across three vendor families — including a contrastive embedding fine-tune and a model whose concept peak sits at depth 0.90 where every prior model peaked 0.2–0.72 — and all three cleared every gate with zero tuning, in minutes each — and on the stricter lexicality-controlled battery, one of the two fresh strangers certifies cleanly and the other posts a borderline null we report rather than hide. That is the defensible core, and it is now evidenced rather than asserted.

Register a model

Review the instrument for the target configuration.

Choose the internal or output-access method that fits the available model access. Inspect its dependencies, controls and limits before use.