open-standards/wai/extensions/determinism-tiers.md

# WAI Extension: Determinism Tiers for Neural Decode

> Status: Draft. Names the **spectrum** of reproducibility guarantees a neural
> capability can make, so a manifest can declare which one it satisfies and a
> verifier knows what it may check. Defines two tiers — `entropy-consistency`
> (the entropy decode reproduces) and `decode-equivalence` (the whole decode to
> the final medium reproduces) — and states which one `intent = replicate` over a
> neural capability promises, what a receipt can hash under each, which WAI
> capabilities are registered at each and at what cost, what the repository
> demonstrates today, and what exactness was measured to cost and what WAI claims from
> that measurement (§7).
> One optional manifest member carries a tier name: a pin's `tier`
> ([SPEC.md §3.1](../SPEC.md)). Otherwise a vocabulary and conformance framing
> over §7.
> Keywords **MUST**, **MUST NOT**, **SHOULD**, **MAY** are RFC 2119/8174.

## 1. Why tiers

Neural decode reproducibility is not binary. A neural decode evaluated in float
through an inference runtime does not, in general, reproduce its reconstruction
across hardware. Each IEEE-754 basic operation (add, subtract, multiply, divide,
square root) is correctly rounded, so it gives the same result for the same
operands everywhere; the differences come from what a runtime may choose per
device — kernels and accumulation order, vector width, fused multiply-add, reduced
precision, math-library functions — and from the process's floating-point
environment. A decoder that also *derives* its entropy parameters in float can,
from one diverging bit, select a different distribution from the encoder's and
corrupt the entropy decode. A float stage whose operations, order, precision and
environment are all fixed does reproduce, within the implementation that fixes
them; §5 describes one, and §2–§3 say why that is not a tier and does not back
`replicate`. Five published routes to reproducibility are below. Two make the
**entropy stage** reproducible while leaving the reconstruction to pixels to the
implementation, within whatever criterion a conformance specification sets on it:

- **The base standard.** JPEG AI (ISO/IEC 6048) defines its bit-exact
  conformance point at the **output of the entropy decoder**, not at
  reconstruction to pixels; its entropy pipeline is integer so that stage
  replicates across devices, while the outputs of later processes "may diverge
  slightly from the reference". One codestream may legally reconstruct through up
  to three synthesis transforms, which a profile or the encoder can restrict.
  Bit-exact picture reconstruction was left out of the first version on purpose.
  An overview of the standard (arXiv 2510.13867) states all of this. It also says
  that the conformance part, ISO/IEC 6048-4, "specifies the requirements for
  generating a JPEG AI-compliant reconstruction", with a testing suite for each
  profile and level pair, and "considers the possibility that a decoder is
  conformant to the standard without having bit-exact reconstruction" — "the
  possibility of standard compliance with a certain leeway". These passages point
  to a criterion around a reference on the reconstruction; the overview does not
  state one. Part 4 was a draft when the overview was written; this document has
  not read it, so it does not say whether Part 4 sets such a criterion, of what
  form, or how wide.
- **Explicit parameter transmission.** A published cross-platform learned video
  codec (arXiv 2606.28027) transmits its entropy model's **scale parameters**
  explicitly, through the hyperprior, so the decoder reads them instead of
  deriving them and the entropy decode is consistent across devices *without*
  bit-exact arithmetic. The side information costs bitrate. Same target: a
  reproducible entropy stage, a reconstruction that may vary. The paper's body
  states the second half: "floating-point differences still cause gradual
  divergence in the features and reconstructed frames", and "without a
  synchronization mechanism, the divergence can grow arbitrarily large" (§3.2).
  It bounds the divergence with periodic I-frames that "fully resynchronize
  encoder and decoder feature states" (§3.2). Those I-frames are decoded by the
  same float model (Appendix B.8), so a resynchronisation moves the picture back
  to one float decode; it does not make the picture reproducible.

The other three make the reconstruction itself reproducible, each by decoding in
integer arithmetic:

- **A quantised decoder.** A published learned image codec (arXiv 2312.11209)
  quantises the weights and activations of all its decoder subnetworks, without
  accumulator overflow, so that the reconstructed image is deterministic across
  devices, and reports its output as bit-exact.
- **Integerisation of a float model.** A published learned video codec
  (arXiv 2502.20762) runs its float model in a 16-bit integer mode without
  retraining: each value is mapped to round(512 · v), and its accumulators are
  32-bit, of which it reports that "no overflow issues have been observed"
  (§4.5). That is an observation, not a bound.
- **Integer training.** A published learned video codec is trained as an
  integer-only model from the start, and claims coding consistency across
  platforms from integer decoding in the encoder's reconstruction loop
  (OpenReview `KCQo0fXtFH`, abstract). Its body and its code were not read here.

One more published line reaches neither tier: **float synchronised on a tested
hardware class.** A streamable learned video codec (arXiv 2608.00483) decodes its
P-frames in 16-bit float, and its I-frames and its periodic resets of a feature
adapter in 32-bit float (§II-C, §II-D). It was tested on twelve GPUs of one
vendor under one driver (Table II). In the mixed mode, a stream decoded only on
GPUs of the generation that encoded it, and at 1080p not on every one of them; in
pure 32-bit float, streams decoded across generations at 1080p and split into
three groups at 4K (Table IV). A
stream decoded outside its group fell from 26.3 dB to between 11.9 and 13.7 dB of
luma PSNR at 1080p, and from 28.2 dB to between 7.3 and 10.6 dB at 4K, or the
decoder did not complete (§II-F-3, Table IV). The authors give the cause SPEC §7
gives (*Replicate-soundness*): pure 32-bit float "is strictly non-deterministic,
with each GPU architecture generating a unique bitstream", because kernels
"whose differing reduction orders break bit-exactness even under IEEE-754 FP32,
which fixes per-operation rounding but not operation ordering" were chosen per
generation (§III-B). Exactness found on the devices tested is a property of those
devices and not of a capability (§2), and the frames between two resets have no
portable hash, so this line is no tier of this document (§3, *No tier between the
two*).

WAI names the two outcomes as tiers, so a capability states which one it makes, an
envelope's `intent` can demand the one it needs (`replicate` over a neural
capability demands `decode-equivalence`, §3), and a receipt or a provenance binding
([reconstruction-binding](reconstruction-binding.md)) can do the same.

## 2. The tiers

### `entropy-consistency`

The **entropy decode** reproduces byte-for-byte across devices — the decoded
symbols (the entropy-decoder output) have a portable hash — but the reconstruction
to the final medium (pixels, samples) MAY differ between conforming sinks. A sink
achieves this either by **integer entropy arithmetic** (deriving the coding
parameters with fixed-point ops, as WAI's integer decoders and JPEG AI's entropy
pipeline do) or by **explicit parameter transmission** (the codestream carries the
parameters, so no bit-exact derivation is required).

- Conformance criterion: *entropy-equivalence* — a verifier decodes the entropy
  stage and compares the decoded symbols, NOT the pixels.
- What a receipt can hash: the codestream and the decoded symbols. The latent
  formed from the symbols is hashable only where every step that forms it (adding
  a predicted mean, say) is itself integer or transmitted. The reconstruction has
  no portable hash, so an energy figure at this tier binds to the codestream and
  symbols, never to an output every conforming sink reproduces
  ([energy-binding](energy-binding.md) §3); a reconstruction that one
  implementation pins is that implementation's (§3).
- JPEG AI sets its bit-exact conformance point at this tier, and explicit
  parameter transmission reaches it (§1). The overview also says JPEG AI's
  conformance part "considers the possibility that a decoder is conformant to the
  standard without having bit-exact reconstruction" (§1); whether that part sets a
  criterion on the reconstruction is not read here, and any such criterion is not
  one of this document's tiers. WAI registers `wai.image.jpegai` here (§5). The
  tier is **necessary** for a sound neural `replicate` under SPEC §7 — without it
  the decode desyncs — but **not sufficient**, and it MUST NOT back `replicate`:
  `replicate` promises the reconstruction, and this tier leaves the
  reconstruction free.

**Why transmitting parameters stops at this tier.** A learned entropy decode
desyncs when the decoder derives an entropy parameter (a scale, and from it the
distribution the next symbol is decoded with) that selects a different distribution
from the one the encoder used. Transmitting the parameter removes that derivation
from the decoder: every decoder reads the same value and decodes the same symbols,
whatever arithmetic its networks run in. Nothing after the entropy decoder is
constrained. The steps that turn symbols into a latent and the synthesis transform
still run in the arithmetic the device provides — float, reduced precision, fused
or reordered accumulation — and, unless the implementation fixes that arithmetic,
their results differ by device. A codestream can
pin *values*; it cannot pin the arithmetic a network is evaluated in, and the
reconstruction depends on that arithmetic. So a portable hash of the reconstruction
needs every stage after the entropy decode fixed as well, which is the next tier.
The two routes compose: a capability MAY transmit parameters *and* pin its
arithmetic.

### `decode-equivalence`

The **whole decode to the final medium** reproduces byte-for-byte on every
conforming sink — the reconstructed pixels (or samples, or attributes) have a
portable hash. The capability's registration pins **one** reconstruction and
specifies it in integer / fixed-point arithmetic end to end, entropy *and*
synthesis, so the output depends on nothing a build, a device or a process
chooses. Where the base codec admits several conformant reconstructions, this
tier fixes one. This is what `intent = replicate` over a neural capability
promises (§3).

A tier is a property of the capability as registered, not of one implementation.
An implementation that pins a reconstruction by evaluating float stages in a fixed
order (§5 describes one) reproduces it only under conditions of its own build and
process (§3). That is a property of the implementation, stated as such; it does
not place the capability at this tier.

- Conformance criterion: the medium's `*-equivalence` (e.g. *decode-equivalence*
  for `wai.neural.int_hyper`, *sample-equivalence* for `wai.audio.int_codec`,
  *splat-equivalence* for `wai.splat.int_codec`).
- What a receipt can hash: everything the lower tier can, plus the reconstructed
  medium itself as the capability's canonical buffer (canonical `RGB8` for images,
  per [reconstruction-binding](reconstruction-binding.md); int16 PCM; the attribute
  tensor; a per-frame hash for video), under the descriptor that pins the
  reconstruction. This is the tier at which an energy figure binds to an output a
  verifier can reproduce ([energy-binding](energy-binding.md) §3).
- Where a base standard sets its conformance point at the entropy decoder (JPEG
  AI, §1), a capability over it reaches this tier only by registering one
  reconstruction in integer arithmetic; `wai.image.jpegai` has not (§5). For any
  capability this tier is the precondition for a hard provenance binding over the
  *rendered* content that every conforming sink can check — a decoded-pixel hash
  is only portable when the reconstruction is pinned.

A capability satisfying `decode-equivalence` satisfies `entropy-consistency` by
construction; the reverse does not hold.

### What a tier covers

A tier is a property of the **whole path from codestream to the medium that is
hashed, bound, or presented** — not of the decode stage in isolation. A sink that
decodes conformantly and then alters the result has not satisfied the tier,
however faithful the decode itself was.

This is not hypothetical. Published and draft standards define mechanisms that
change the medium after, or alongside, a conformant decode, and they arrive
through the same pipe as the codestream:

- **Post-decode neural filters.** ITU-T H.274 (V4, 01/2026) `nnpfc` / `nnpfa` SEI
  messages activate a neural post-filter after decoding, changing output pixels.
  The filter may be named by URI or carried in-band.
- **Energy-saving presentation and quality-recovery metadata.** ISO/IEC 23001-11
  (green metadata) defines metadata with which a receiver changes the decoded
  pictures, before display or inside the decode:
  - *Attenuation-map information*, carried in an SEI message with the attenuation
    map as an auxiliary picture, describes post-processing to apply to the decoded
    pictures so the display emits less light. A receiver can also ask the
    transmitter for such maps with a display attenuation map power reduction
    request (DAMPR-Req); the maps it receives are applied to the decoded content
    in the same way.
  - *RGB-component statistics and quality levels* drive display adaptation. The
    receiver chooses a quality level, scales the RGB components of the
    reconstructed frames by the peak signal over that level's
    `max_rgb_component`, and lowers the backlight or voltage in proportion
    (Annex B.2). At any level other than the no-quality-loss operating point,
    components above that level's `max_rgb_component` are saturated; for contrast
    enhancement, components at or below `lower_bound` are set to zero and those at
    or above `upper_bound` to the peak signal (clause 7.4).
  - *Quality metrics for cross-segment decoding*: a decoder uses them to decide
    whether to enhance the first picture of a low-quality segment from the last
    picture of the previous segment, and the enhanced picture becomes the
    reference for the pictures that follow.

  The standard notes that its metadata can also be used "to get larger energy
  savings, but at the expense of some QoE degradation".

Other 23001-11 mechanisms leave the decode as it is. Three of them change the
payload before it arrives:

- A decoding operation reduction request (DOR-Req) goes from the receiving device
  to the **remote encoder** in a point-to-point session. The encoder produces a
  less complex bitstream and can answer with a response (DOR-Resp) saying how it
  decided to answer.
- A display power reduction attenuated video request (DPRAV-Req) asks the remote
  encoder to apply an attenuation map to the video before encoding it. The
  receiver then gets an attenuated video, and the response (DPRAV-Resp) states the
  energy reduction rate and quality it can expect.
- Low-power encoding, which produces the low-quality segments that cross-segment
  decoding repairs, alternates high-quality and low-quality segments to reduce the
  encoder's power.

In each case the sink decodes what it receives like any other bitstream, but the
payload is not the one that would have been sent otherwise. The remaining two
alter neither a payload nor a decode's output. Media-selection metadata lets an
adaptive-streaming client choose among the available representations by their
decoder- and display-power characteristics; what it receives is one of those
representations as published. Complexity metrics let a decoder vary its operating
frequency, which changes what a decode costs, not what it outputs.

All of this is metadata for saving power, stated in advance; none of it reports
energy that was spent. Its energy quantities are expectations: an attenuation
map's expected energy saving rate, and the rates a receiver asks for and a
transmitter says the receiver can expect, each a percentage (clause 7.4); a
representation's decoder-operation reduction ratios and its maximum potential
display power saving (clause 8). Complexity metrics are proportions of decoding
operations over a period (clause 6.2), and a DOR-Resp acknowledges what the
encoder changed. So a sink that acts on this metadata has a prescription, not a
measurement, and a tier claim or receipt over its output is bound by the rules
below like any other.

This section accounts for every metadata set that the Introduction of the
4th-edition CD text (WG 3 N1864, dated 2026-08-28) names. Read in that text: the
Introduction, and in part clauses 6, 7, 8 and 9 and Annex B.2, B.4, B.5 and B.6.
The published edition was not read.

The rules:

- A sink claiming a tier MUST evaluate it over the medium it **emits**. If it
  applies processing to the decoded medium that alters it — after decode, as
  attenuation maps and display adaptation do, or fed back into the decode as
  cross-segment decoding does — it MUST either not apply the processing, or MUST
  NOT claim the tier for the emitted result.
- A tier claim, a decoded-medium hash or an energy figure over a payload that an
  encoder altered to save energy — at a receiver's request, such as a
  decoding-operation-reduction or attenuated-video request, or on its own
  account, as low-power encoding does — covers that payload, not the one it
  stands in for, and a receipt over it MUST NOT be presented as covering the
  payload it stands in for. Spending fewer joules by computing or emitting less is
  a fidelity choice; a receipt that reports the joules while concealing the
  degradation states a cost without its consideration.
- A capability's registration SHOULD state whether its payload can carry
  post-decode processing directives at all. A capability whose payload cannot is
  unaffected by this section, and saying so is cheaper than leaving it open.

The general rule, which is why this section exists: **a determinism claim covers
what the sink emits.** A hash over a decode that something later modified
describes an artifact nobody saw — the hash verifies, and it attests the wrong
thing.

A pinned integer derivation run over the decoded medium is processing that alters it, and
is the one kind that keeps the tier: `wai-synthesis:upscale2` and `wai-synthesis:denoise3`
([synthesis](synthesis.md) §4), and the super-resolution derivations of
[exact-upscale](exact-upscale.md) §3 under a prior the sink holds by its SHA-256. Each is a pure
integer function of the decoded samples and its pinned parameters, so a sink that emits its
output holds the tier of the decode it ran on, and states the derivation, and its prior, with the
output (synthesis §9.2, exact-upscale §6).

HDR display adaptation is such processing: a sink makes, from the decoded picture, a gain map
or dynamic metadata and its display's headroom, the rendition it presents, and the standards
that define it state the computation in real numbers, so the presented pixels differ from sink
to sink. The derivations of [exact-tonemap](exact-tonemap.md) §4 and §5 pin it in integer
arithmetic: a rendition is a pure function of the decoded samples, the companion's bytes and
the headroom, so a sink that presents one holds the tier of the decode it ran on, and states the
headroom and the rendition's digest in its rendition record (exact-tonemap §7).

## 3. Declaring and using a tier

- **Declaring.** A capability's registration (SPEC §5) states its tier via its
  `*-equivalence` criterion, and its registry class carries it (SPEC §7):
  `NeuralIntegerExact` capabilities are registered at `decode-equivalence`,
  `NeuralEntropyExact` capabilities at `entropy-consistency`, and `NeuralFloat`
  capabilities claim neither. §5 lists WAI's capabilities at each tier. A
  capability MAY declare `entropy-consistency` when it only guarantees the entropy
  stage. A pin MAY also state a tier in the manifest, as `tier` (SPEC §3.1), from
  the closed vocabulary `decode-equivalence`, `entropy-consistency`, or `none` for
  neither; an encoder writes only those tokens, and a sink reads any other token
  as `none`. A stated tier above the capability's registered tier does not conform
  (`tier_exceeds_class`): the `replicate` verdict does not rest on it.
- **Over a neural capability, `replicate` promises `decode-equivalence` (SPEC
  §7).** An envelope with `intent = replicate` over a neural capability MUST be
  over a capability registered at `decode-equivalence`, which SPEC §7 admits only
  by the integer construction: an integer / fixed-point decode path with no float
  operation. Such an envelope MUST pin the parameter set of that capability with a
  pin that declares `decode-equivalence` (SPEC §3.1, §7).
  `entropy-consistency` MUST NOT back `replicate`. A neural decode that derives
  its entropy parameters in float meets neither tier; one that reads them from the
  codestream, or derives them in integer arithmetic, and runs its later stages in
  float meets `entropy-consistency` only. A reconstruction that one
  implementation pins through fixed-order float stages does not back `replicate`
  either: what makes such a stage reproducible — a build that emits no fused or
  reassociated operations, the default floating-point environment in the running
  process, library functions whose last-place error cannot reach the output —
  belongs to one implementation and one process, and neither the envelope nor the
  registry can state it. That is why `wai.image.jpegai` is registered at
  `entropy-consistency` and does not back `replicate` (§5).
- **Over a standard codec, `replicate` promises the named standard's decoder
  conformance (SPEC §7, *What `replicate` promises over a standard codec*).** For
  some standards that is the same bytes on every sink; others hold decoders to a
  criterion around a reference — numeric thresholds in the clauses read for Opus
  and JPEG XL; for JPEG and JPEG 2000 a criterion whose bound the text read does
  not state (SPEC §7); and, for AV1 when film grain is signalled, a perceptual
  criterion the AV1 specification leaves undefined — and over those `replicate`
  promises that criterion, not byte identity. The tiers in this document describe
  neural capabilities. `wai.image.jpegai` has a named standard too, and the
  overview's account of its conformance part (§1) points to a criterion around a
  reference on the reconstruction; if Part 4 states one, that would put it among
  the standards whose conformance `replicate` could promise. That part has not
  been read here. Holding `wai.image.jpegai` to
  `decode-equivalence`, and so refusing `replicate` over it, is a conservative
  choice pending that reading, not a finding about the standard; SPEC §7
  (*`wai.image.jpegai` and the standard classes*) states the choice and how it
  differs from the choice made for the standard codecs.
- **What the reference impl checks.** `decode_envelope_strict` applies SPEC §7 in
  two steps (`codecs::replicate_refusal`). First the capability's registry class:
  only `NeuralIntegerExact` passes among the neural classes (the three standard
  classes, which are not neural, are not refused by this rule). The class does
  not depend on anything the sender declares, so a `replicate` envelope over `wai.image.jpegai` or a float
  codec is refused even when its prior declares an integer contract. Then, for an
  integer-exact capability, the pin: it must declare `decode-equivalence` — by
  `tier`, or, for a pin without `tier`, by the reading of its `determinism` string
  that SPEC §7 fixes, a substring match (a contract containing `int`, `fixed` or
  `integer` and none of `f32`, `f64`, `float`). Either way it verifies a
  declaration, not the arithmetic. Neither step inspects the decode path a sink
  actually runs; for `wai.neural.int_synth` and `wai.neural.int_mlicpp`, the
  `NeuralIntegerExact` capabilities the reference neural dispatch decodes, there is
  only the integer path. The decode then reports the path that ran
  (`neural::Dispatched::path`), which a sink states in a receipt (§5).
- **Receipts.** A receipt that carries a decoded-medium hash as reproducible by
  any conforming sink MUST come from a `decode-equivalence` capability. A hash
  over a reconstruction that one implementation pins (§5) is that
  implementation's, and a receipt MUST NOT present it as the capability's.
- **No tier between the two (normative).** The tier vocabulary of a pin is closed:
  `decode-equivalence`, `entropy-consistency` and `none` (SPEC §3.1). No tier,
  registry class or claim states that a reconstruction stays within a bound of the
  exact one between points at which it is made exact again. Such a bound is
  measured on named devices and a named corpus, and a sink cannot check it from the
  bytes it received (§4); §1 gives the published line that reaches it. Three rules
  follow:
  1. A decode of a `NeuralIntegerExact` capability that runs any stage in reduced
     precision or in floating point is not a decode of that capability. A sink
     MUST NOT report it under that capability's identifier, in a receipt or in a
     claim. A faster decode of the same payload is registered as a capability of
     its own, with its own class, as `wai.audio.opus.dred` and
     `wai.audio.opus.dred.int` are two capabilities over one payload (SPEC §5,
     *Broadcast and streaming codecs*).
  2. A capability whose payload holds frames of more than one type, such as intra
     frames and predicted frames, takes the class, and with it the tier, of its
     weakest frame type. A pin's `tier` MUST NOT state more than that class gives
     (SPEC §3.1, `tier_exceeds_class`). A frame of such a payload decoded exactly
     does not raise the tier of the capability.
  3. A clip digest (SPEC §7, *Digests*), a stage hash
     ([staged-delivery](staged-delivery.md) §5), or a reconstruction binding over
     a sequence of frames, covers only frames that were each decoded at
     `decode-equivalence`. A sequence in which any frame was decoded at a weaker
     tier, or by a path that does not compute the registered reconstruction, has
     no such digest. Each frame of it that was decoded exactly keeps its own. A
     receipt is checked per object, not per frame: a stage hash is refused where any
     decoder entry decoded the set's base at a weaker tier, or the base or a layer of
     the set on a path that is not exact, whichever units that entry presented
     ([jwp-receipts](jwp-receipts.md) §2.4.12, step 6 against the stage claims,
     item 5). So the outcome does not depend on whether the verifier holds the
     outputs, which alone say what each entry presented.

  These rules govern decoded media. A generated medium's tier,
  generation-equivalence (SPEC §7, `GenerativeDeterministic`), is not a pin's tier:
  it carries its own digest of the frames generated
  ([synthesis](synthesis.md) §9.2; [avatars](avatars.md) §7.7), and rule 3 does not
  void it.

  A receipt of a decode states the path that ran ([jwp-receipts](jwp-receipts.md)
  §2.4.12, a decoder entry's `variant`), and the claim corpus refuses a receipt
  that breaks each rule: a `NeuralIntegerExact` capability named with a path that
  is not exact (rule 1); a pin's `tier` above its capability's registry class,
  which is that of its weakest frame type, and above what the path that ran holds
  (rule 2); and a stage hash over a base or a layer one of whose decoder entries
  decoded it at a weaker tier or on a path that is not exact (rule 3).
- **Post-decode processing.** A tier claim is void if the sink alters the medium
  after decode (§2, *What a tier covers*). This applies to the emitted result,
  not to the decoder's internal conformance.
- **Provenance.** The [reproducible-reconstruction binding](reconstruction-binding.md)
  — a C2PA / JPEG-Trust hard binding over decoded pixels — is checkable by every
  conforming sink only over a `decode-equivalence` capability; over an
  `entropy-consistency` capability only the entropy-stage output is bindable that
  way. [reconstruction-binding](reconstruction-binding.md) §1–§2 also define a
  binding over `wai.image.jpegai` as a binding over its exact decoder's pinned
  reconstruction; that binding is checkable only by that decoder under the
  conditions in §5–§6.
- **Staged delivery.** A unit refined in stages holds the weakest tier of its base
  and every layer applied to it, and `entropy-consistency` does not pass through a
  layer ([staged-delivery](staged-delivery.md) §5).

## 4. Framing

Both tiers here are **reproducibility** guarantees: a sink can check them from the
bytes it received. A separate axis exists for media whose correctness is a
downstream model's behaviour rather than a byte comparison — see
[task-fidelity](task-fidelity.md), which is a sibling rather than a third tier
precisely because such a claim is *not* sink-checkable from the bytes.

The two tiers are named so that each capability states which one it makes.
`entropy-consistency` is the tier at which JPEG AI sets its bit-exact conformance
point, and the tier WAI registers `wai.image.jpegai` at. `decode-equivalence`
pins one full reconstruction, so the rendered result, not only the entropy
stream, is reproducible and hashable; it is what `replicate` over a neural
capability promises. §5 lists the WAI capabilities at each tier, with what the
tier costs them.

## 5. Where WAI offers each tier, and what it costs

### `decode-equivalence`

| capability | hashable medium | how the reconstruction is pinned |
|---|---|---|
| `wai.neural.int_synth` | RGB | integer synthesis (post-training quantisation of a float model, with per-layer shifts calibrated on measured activations; of the committed vectors only `photo` is such a model, and `gradient`, `bloom`, `facade` and the cross-architecture golden of §6 use seeded integer weights) |
| `wai.neural.int_hyper` | RGB | integer hyper-decoder, integer scale → distribution step, integer synthesis (post-training quantisation of a float model) |
| `wai.video.int_hyper` | per-frame | an `int_hyper` keyframe, then integer motion-compensated P-frames |
| `wai.audio.int_codec` | int16 PCM | integer synthesis over a factorized rANS latent |
| `wai.splat.int_codec` | attribute tensor | integer synthesis over a factorized rANS latent |
| `wai.neural.int_mlicpp` | RGB | integer hyper-decoder; an integer entropy network re-run before each of the `2·K` channel-slice × checkerboard passes, integer scale → distribution step; integer synthesis. Stream values an exact decode cannot hold are refused, not wrapped |

| `wai.audio.opus.dred.int` | the feature sequence (i32 at `2^16`) | the draft's DRED feature decoder as the `wai-ops/1` graphs of `dred-int/1`, over the draft's own weights file pinned by SHA-256: weights quantized from their float32 bits, integer activations, `tanh` and the sigmoid as integer tables ([opus-dred](opus-dred.md) §4). The vocoder that renders the features is the sink's and outside the claim |

These seven are integer throughout, are the capabilities the registry classifies as
`NeuralIntegerExact`, and are the neural capabilities that back `replicate`.

### `entropy-consistency`: `wai.image.jpegai`, and its exact decoder's pinned reconstruction

The registry classifies `wai.image.jpegai` as `NeuralEntropyExact` and registers
it at `entropy-consistency`. **This is a downgrade:** earlier in v0.4.0 the
capability was listed in the table above and classified `StandardDefined`, and the reference
strict check passed a `replicate` envelope over it whenever the prior declared an
integer contract. It moved because its reconstruction runs float stages and the
reference sink can decode it through a float ONNX Runtime path, so
decode-equivalence never held as a property of the capability (SPEC §7).

| stage | arithmetic |
|---|---|
| entropy decode (mANS) and the scale-index derivation (an integer hyper-scale network) | integer on both reference decode paths — what the capability guarantees |
| hyper-decoder, context-model and synthesis networks | 16-bit-PTQ integer in the exact decoder; float through ONNX Runtime in the fallback |
| dequantisation, latent refinement, the post-filter chain (the eICCI post-filter is a float CNN), colour conversion | IEEE-754 float in a fixed evaluation order in the exact decoder; float in the fallback |

**The exact decoder's pinned reconstruction.** WAI's exact decoder
(`jpegai_decode::decode`) pins one reconstruction to canonical `RGB8`: the
high-operation-point synthesis, then the post-filters, in the arithmetic above. It
reproduces across machines when the conditions in §6 hold: its float stages use only
IEEE-754 basic operations in the fixed order of the source, with the gain scaler's
`exp` and every rounding built from those operations (`float_repro`), so no platform
math library is on the path; the target evaluates at the operands' own precision;
and the floating-point environment is the default. The decoder checks the last two
before decoding (`jpegai_decode::check_scope`). Those conditions belong to this
implementation. That is why the pinned reconstruction is not a tier, does not back
`replicate`, and is bindable only as
[reconstruction-binding](reconstruction-binding.md) defines. On one codestream, the
decoded bytes, and the float values the decoder reports after each stage, have been
measured identical on four platforms (§6); values inside a stage are not compared
directly.

The exact decoder implements one configuration, which its tests exercise.
`jpegai_decode::check_codestream` accepts a codestream only when its headers select
exactly that configuration:

- a 256×256, 8-bit, 4:4:4 picture with no display cropping and the built-in
  YUV→RGB colour transform;
- the high-operation-point synthesis (`synthesis_transform_id` 2);
- `model_id` 0, whose gain, LSBS and LEF tables and residual-scaling constants are
  tools_0's;
- `beta_displacement_log` 0 for luma and chroma. The exact decoder does not
  implement the quantiser's β displacement, so a codestream with any other β is
  refused, not decoded;
- 160 luma and 96 chroma latent channels, one entropy substream per plane, and no
  region partitioning, cube flags, 3-D gain or synthesis tiling; LSBS, residual
  variance scaling and GRFS enabled;
- post-filters only in the forms implemented: one single-tap linear enhancement
  filter per chroma plane; eICCI untiled on all three planes with the model pair the
  weight bundle carries (short-list indices 0 and 1 — the bundle format does not
  record its pair, and the exporter emits only that one); the nonlinear enhancement
  filter untiled and unmasked, with eight weights; LEF on a luma channel;
- both headers parse to the end of their segments, and the SOZ, SORP and SORS
  substreams are present with no SOQ segment.

That is narrow. Of the reference encoder's five default rate points
(`cfg/BRM/default.json`), only the first selects `model_id` 0 with β = 0; the
others select `model_id` 1–3 or a non-zero β and are refused, as is any codestream
to which the encoder's bitrate matcher assigns a non-zero β.

The float ONNX path hard-codes the same models, tables and filter selection, and
refuses the same codestreams, so the reference sink refuses a codestream outside
this configuration rather than decoding it to the wrong image. Within it, the exact
decoder runs when the sink supplies its integer weight bundle and the thread's
floating-point environment is the IEEE-754 default. Otherwise the reference sink
decodes the same capability ID through the float ONNX Runtime path, whose
reconstruction is not pinned. The reference dispatch reports which path ran:
`neural::decode_envelope` returns, with the image, the capability it decoded
through and the path, `DecodePath::Exact` or `DecodePath::Float`. A sink states that
path in a `reconstruct-session` claim as its decoder entry's `variant`, `exact` or
`float` ([jwp-receipts](jwp-receipts.md) §2.4.12), and the reference check of a
reconstruction binding reads it (`neural::check_reconstruction_binding`,
[reconstruction-binding](reconstruction-binding.md) §2).
`wai-rs/tests/jpegai_decode_path.rs` runs each path and requires the result, the
claim and the binding check to say which ran. CI runs the exact decoder over the
pinned bundle; the float path's model bundle is not in the repository, so its case
runs locally only, on a host that holds it.

On both paths the stages the capability's claim covers, the scale path and the two
entropy decodes, run through the integer operator set, by the lowering `jpegai-int/1`
([integer-operators](integer-operators.md) §13.6.1) at the profile `acc32`. Their
refusals have codes: `model_mismatch` for a bundle that fails a load check of §13.6.1
or whose scale graphs do not meet `acc32`, and `stream_exhausted` for a latent or
hyper-latent substream that ends before the decode it carries. The exact decoder runs
its hyper decoders, context stages and synthesis through the same operator set too, by
`jpegai-int/1` (§13.6.2) at the profile `acc64`: those graphs' outputs lie outside the
claim, and their refusals are `model_mismatch` for a weight file that fails §13.6.2's
load checks or networks that do not meet `acc64`, and `field_range` for a dequantised
residual, a latent or an activation that leaves i32. A sink that holds the
integer weight bundle and refuses the exact decode reports that refusal, with its code;
it does not then decode the codestream through the float path. Only a sink without the
bundle, or a thread or target that cannot run the exact decoder's float stages, takes
the float path. A decoded-pixel hash or a
[reconstruction binding](reconstruction-binding.md) over `wai.image.jpegai` holds
only for output of the exact decoder.

### What `decode-equivalence` costs

The tier adds nothing to the wire: nothing is transmitted beyond the capability's
own codestream. The costs fall elsewhere, and the JPEG AI exact decoder pays the
same ones for its integer networks:

- **Fidelity to the float model.** The quantised networks do not reproduce the
  float model's output, so the pinned reconstruction differs from it. For
  `wai.neural.int_hyper`, over one pretrained model (`mbt2018_mean`, quality 6)
  and one pinned test photograph, the integer decode of the full conditional
  bitstream measured 35.985 dB PSNR against 36.175 dB for the same model's float
  decode, a cost of 0.19 dB (macOS arm64; `wai-rs/src/int_hyper_synth.rs`).
  Quality is image-dependent, and this compares the model only with its own float
  decode. For the JPEG AI exact decoder, `wai-rs/tests/jpegai_e2e_full.rs` compares
  against the reference decoder's output on one 256×256 reference codestream and
  asserts max|Δ| ≤ 2 with at most 5% of samples differing; the recorded result is
  max|Δ| = 1 on 4.446% of samples (macOS arm64, release build, 2026-09-27). CI
  jobs re-check both bars: `wai-sota-vectors.yml` requires the `int_hyper` cost to
  stay within 1 dB on that photograph, and `wai-jpegai-e2e.yml` runs the JPEG AI
  comparison and requires the decoded pixels to hash to a pinned value. Both
  have run on the forge's Linux runner and passed on every run since 2026-09-28
  (§6). Across all eight published qualities of the model and 24 pictures, the
  cost in rate at equal quality is measured in §7.
- **Compute.** The decoders are CPU code in integer arithmetic (i64, with i128
  intermediates in places; plus fixed-order float for the JPEG AI exact
  decoder's float stages, which stay single-threaded). Their convolutions, IGDN
  and the JPEG AI synthesis's attention and element-wise steps run through
  `int_kernels`: register-blocked tiles, NEON widening multiply-accumulate on
  aarch64 (portable Rust elsewhere), and worker threads on native targets (one
  worker on wasm). Each fast kernel gives the same integer for every output as
  the scalar reference kernel kept beside it, which stays selectable at run
  time (`Policy::reference()`). They cannot use the float GPU / NPU paths a
  float learned decoder runs on. Measured throughput, on one machine and with
  its limits, is in [`research/decode-throughput.md`](../research/decode-throughput.md)
  §4–§5; no claim in this document depends on those figures.
- **Model preparation.** A model is offered at this tier only once exported into
  the decoder's pinned form, sink-supplied: quantised weights and requantisation
  constants for the integer networks (and, for the JPEG AI exact decoder's eICCI
  post-filter, float weights with BatchNorm folded in). A float-only model is not
  offered at this tier as it stands.

Parameter transmission pays in bitrate and leaves the decoder's arithmetic free;
the integer decode pays in quantisation, compute and model preparation and adds no
bits. A capability's tier states which trade it made.

### Where `decode-equivalence` holds: fabrics, and output a sink cannot read

The tier is a property of the integer arithmetic a capability's registration fixes,
so a fabric meets it when it executes that arithmetic exactly. What this revision
shows, by fabric:

- **CPU, native.** The reference decoders, whose fast kernels give the same integer
  for every output as the scalar reference kernels (*Compute*, above). The
  golden-byte suite passes on Linux x86_64, Linux arm64 and macOS arm64 (§6).
- **WebAssembly.** The same Rust compiled to wasm32. `wai-web-wasm.yml` builds the
  browser crate for `wasm32-unknown-unknown` and, under Node, requires the `int_synth`
  corpus and the committed `WIV1`, `WIH1`, `WIA1` and `WIS1` vectors to decode to the
  hashes the native decode gives; it passed on the forge for main at `412a08c5`
  (2026-10-09). The `int_mlicpp` vector decodes to its pinned bytes on
  `wasm32-wasip2` under wasmtime (§6). The browser crate's release build contains no
  SIMD instruction: on the source of the change that added this subsection it
  validated with WebAssembly's SIMD and relaxed-SIMD features disabled
  (`wasm-tools validate`). CI (`wai-web-wasm.yml`) validates both of the crate's
  bundles and the package of record, `reach/record/wai_web_bg.wasm`, with relaxed SIMD
  and threads disabled, and fails on a module that uses either. CI does not check that
  they contain no other SIMD instruction. A WebAssembly decoder at this
  tier MUST NOT use relaxed-SIMD instructions, whose results the WebAssembly
  specification lets engines choose among, and MUST NOT depend on the bits of a NaN.
- **GPU.** A decoder's `wai-ops/1-i32` graphs ([integer-operators](integer-operators.md)
  §9.1) run on a GPU through `wai/wai-ops-gpu`: WGSL compute kernels in 32-bit words, at
  the `acc32` widths for a graph whose certificate meets that profile, and otherwise in
  64-bit and 128-bit arithmetic built from 32-bit words, an `acc64` implementation
  (integer-operators §7.3). The host makes the interpreter's run-time checks before the
  device runs anything, and a `check` tensor that leaves its range refuses the decode at
  its operator. The driver's stage operators stay on the CPU, and so does a graph with an
  operator outside `wai-ops/1-i32`. The tier is claimed for those graphs only under the
  conditions of *Claiming the tier on a GPU* (below), which the crate meets, and only on
  an adapter where a run, on the kernel text of this revision, gave WGSL's result for
  every guarded case of the integer semantics suite, the host's result for every word of
  the arithmetic probe, and the interpreter's outputs, at both widths, for every graph
  vector of integer-operators §14.1 and §14.2 that `wai-ops/1-i32` covers (§6), with the
  run's report committed (`wai/research/ops-gpu-runs.txt`). That is each adapter and backend
  below:
  - Metal, on the integrated GPU of an arm64 desktop (macOS 27.0.1), in the forge's run
    10236 (job 46928) of `wai-ops-gpu.yml`;
  - a browser's WebGPU on that same GPU, the kernels compiled by the browser's own WGSL
    compiler (Chromium 148, headless), in the same job;
  - Vulkan, on an integrated GPU of PCI vendor 0x1002, device 0x1586 (Mesa 26.0.8), Linux
    x86_64, and a software Vulkan driver (llvmpipe, Mesa 26.0.8) on that host, which runs on
    the CPU.

  On Metal, the capability corpora (integer-operators §14.4) also decoded with their
  graphs on the device, as §6 records: every committed vector and refusal fixture of
  `wai.neural.int_hyper`, `wai.video.int_hyper`, `wai.audio.int_codec`,
  `wai.splat.int_codec`, `wai.neural.int_synth` and `wai.neural.int_mlicpp` gave the bytes,
  or the refusal code, of the decode on the CPU, and every graph the device ran gave the
  interpreter's outputs. `wai.image.jpegai`'s scale graphs (`hs_y`, `hs_uv`, at `acc32`)
  and hyper decoders and context stages (`rec_y`, `rec_uv`, at `acc64`) ran there with the
  interpreter's outputs, so its `entropy-consistency` claim (§5) holds with its scale path
  there. Its synthesis graphs use operators outside `wai-ops/1-i32` and ran on the CPU. On
  the Vulkan adapters the capability corpora have not run on this revision's kernels. The
  adapters G1 showed on the kernel text before this revision (Vulkan on Linux x86_64 with
  PCI vendors 0x10de and 0x8086), and those a hand run showed on this revision's kernels
  with no record committed (Vulkan and DX12 on a Windows x86_64 host, and its software DX12
  adapter), are not claimed until a run of this revision's kernels is recorded there. The kernel text differs from G1's only in the
  shift masks and the two clamps that *Claiming the tier on a GPU* requires. Nothing is
  claimed for an adapter, a backend or a driver version that is not listed.
  The frames of `wai.avatar.face`, whose tier is generation-equivalence
  ([avatars](avatars.md) §7.8), gave the CPU's bytes with the generator's graph on Metal, at
  `acc64`, and under `wasm32-wasip2` (§6).
- **NPU.** `wai/wai-ops-npu` runs a graph of `wai-ops/1-npu`
  ([integer-operators](integer-operators.md) §9.2) with its arithmetic operators on one
  compute core of a neural processor. The interpreter runs the rest: the plan, the
  checks, the range policies and the movement operators. On the device of PCI vendor
  0x1022, device 0x17f0 (firmware 1.1.2.65, device runtime 2.21.75), a run reproduced
  every graph vector of integer-operators §14.1 and §14.2 whose graph is in the subset
  (§6). Integer-operators §9.5 records that run, and where the device's own arithmetic
  is not exact: its shift-round step at shifts of 60 to 63, and its accumulator lanes
  past their width. No decoder of these capabilities runs its graphs on a neural
  processor in this revision, so the tier is not claimed for a decode there. Nothing is
  claimed for any other neural processor. A network evaluated in floating point on a GPU
  or NPU does not meet it (§1).

The tier covers the medium a sink emits and can hash (§2, *What a tier covers*). A
sink that hands a payload to a protected media path whose output the application
cannot read, as Lane A of [SPEC](../SPEC.md) §6.5 does, holds no output to hash, and
the tier never covers that output: the sink MUST NOT claim the tier for it, or bind a
digest, a reconstruction or an energy figure to it. Content that the application
decrypts itself, as in Lane B of SPEC §6.5, is decoded like any plaintext, and the
tier holds for it as it does for plaintext.

### Claiming the tier on a GPU, and on another accelerator

`decode-equivalence` holds on an accelerator (a GPU, a neural processor, any device other
than the CPU that runs a decoder's graphs) only where three things keep the integer
arithmetic the capability's registration fixes: the program the device runs, the
toolchain's translation of that program into the device's own code, and the device. In
general form, a decoder claims the tier for output an accelerator computed only when, for
every program that ran it: the program computes in integers only, so no reassociation or
fusion can change a result; it uses no operation whose result depends on a grouping the
device chooses; no result depends on scheduling (item 3 below, as written); the
translation gives the program language's result for every operation it uses, and the
program does not reach an operation on operands where the translation does not; the
device passes a conformance suite of the operations' edge cases, and the implementation's
probe of the wider arithmetic it builds, before it runs a graph; and the claim names the
device and the toolchain that ran. The items below are those conditions for a GPU, whose
programs are WGSL modules; another accelerator's text states its own program language,
translator, suite and probe in their place.

The texts read for a GPU are WGSL, W3C Candidate Recommendation Draft, 21 October 2026,
and naga 30.0.1, the translator wgpu 30.0.1 compiles WGSL with.

WGSL defines each integer operation at run time, the cases that are errors when the
operands are known before the shader runs included (WGSL §8.8, §8.10, §17.5): addition,
subtraction, multiplication and negation of a concrete integer type are modulo 2^32;
`e1 / 0` is `e1`, and `e1 % 0` is 0; the most negative `i32` divided by −1 is itself, and
its remainder 0; a shift amount is taken modulo 32; an integer `clamp(e, low, high)` is
`min(max(e, low), high)`; a bit field's offset and count are clamped to the word. The
languages a WGSL module is translated into leave some of these open: MSL and HLSL leave
signed overflow undefined; MSL leaves a left shift of a negative or overflowing signed
value undefined, and a right shift of a negative value to the implementation; and MSL,
HLSL and SPIR-V leave a zero divisor, the most
negative value divided by −1, a shift by the width or more, and a `clamp` whose bounds are
out of order undefined or to the implementation. Floating point is open even in WGSL: an
implementation may reassociate operations, and may fuse them where the result is at least
as accurate (WGSL §15.7.5). A subgroup operation's result depends on its subgroup, whose
size is a power of two from 4 to 128 that the device compiler chooses, whose invocations
have no defined relationship to `local_invocation_index`, and of whose invocations only
the active ones take part (WGSL §15.5, §15.6.3). An out-of-bounds access has several
permitted outcomes (WGSL §6.5.7).

So a decoder **MAY** claim `decode-equivalence` for output whose graphs ran on a GPU only
when every condition below holds for every shader module that ran them, on that adapter,
in that process. Otherwise the output is the GPU's, and no tier is claimed for it.

1. **Integer only.** Every scalar type in the module, of every declaration and of every
   expression, is `bool`, `i32` or `u32`; every literal is of one of them; every
   conversion is between them. No floating-point type appears for any purpose, an index
   or a loop bound included. With no floating-point operation, no reassociation or fusion
   an implementation may make changes a result: integer addition and multiplication
   modulo 2^32 give the same value in any order.
2. **No subgroup or quad operation, and no extension.** The module enables nothing
   (`enable`) and calls no subgroup or quad built-in.
3. **No result depends on scheduling.** Each output word is written by one invocation and
   read by no other invocation of the same dispatch. Every atomic operation on a given
   memory word is of one kind: `atomicOr`, `atomicAnd`, `atomicMin` or `atomicMax`, or
   `atomicAdd` and `atomicSub` together; no `atomicLoad`, `atomicStore`, `atomicExchange`
   or `atomicCompareExchangeWeak` reaches it in the dispatch; and the value an atomic
   returns is not used. Each of those kinds alone is order-free; two on one word are not
   (an `atomicOr` then an `atomicAnd` differs from the reverse). No access is out of
   bounds.
4. **The translation gives WGSL's results.** A translation into a language that leaves an
   integer result open produces WGSL's result for each operation the module uses: signed
   addition, subtraction, multiplication and negation evaluated in unsigned arithmetic
   and reinterpreted; division and remainder through a guard that substitutes the divisor
   1 where WGSL's result is the dividend, and remainder 0; the absolute value of the most
   negative value, itself. naga 30.0.1 does all of these in MSL and HLSL (its `naga_div`,
   `naga_mod`, `naga_neg` and `naga_abs`, and signed arithmetic through unsigned); in
   SPIR-V, whose integer addition, subtraction, multiplication and negation wrap, it
   guards division and remainder. In MSL the division and remainder guards, and in SPIR-V
   the division and unsigned-remainder guards, are written only while its integer-division
   checks are on; SPIR-V's signed remainder is guarded even with them off, and HLSL's
   guards are always written (DX12's translation goes through FXC, shader model 5.1). A
   host
   **MUST NOT** turn a translator's guards off: in wgpu, a module created unchecked, or
   `ShaderRuntimeChecks::int_div_checks` false. Where a translator does not produce
   WGSL's result for an operation, the module **MUST NOT** reach that operation on the
   operands concerned. naga 30.0.1 passes a shift amount to MSL, HLSL and SPIR-V without
   reducing it modulo 32, writes a signed left shift as it is, and writes an integer
   `clamp` as MSL's and HLSL's `clamp`; so every shift amount in the module is a literal below 32 or
   masked below 32 (`e & 31u`), no left shift has a signed left operand (the module
   shifts the `u32` and bitcasts), and every `clamp` has literal bounds in order, another
   bound being written `min(max(e, low), high)`. A right shift of a negative value, which
   MSL leaves to the implementation, is held by item 5: the suite runs it on the adapter.
5. **The adapter passes the conformance suite first.** Before an adapter runs any graph of
   the decode, in the same process, it gives WGSL's result for every case of the integer
   semantics vectors (`wai/wgsl-conformance/integer.jsonl`) that the file marks guarded,
   and for further cases of each operation that the implementation draws, and the host's
   result for every word of the implementation's probe of the wider arithmetic it builds
   from 32-bit words. And the implementation has reproduced, on that adapter, backend and
   driver, every graph vector of integer-operators §14.1 and §14.2 its subset covers, at
   each width it runs (integer-operators §12, *Evidence for a fabric*). An adapter that
   differs on any guarded case or probe word runs no graph.
6. **The claim names what ran.** A receipt or report of a GPU decode at this tier names
   the adapter (backend, PCI vendor and device, driver) and the translator and its
   release. Where the backend reports no PCI identity and no driver (Metal and a browser's
   WebGPU give vendor and device 0x0 and an empty driver), it names in their place the
   adapter name the backend gives and the operating system's build, or the browser and
   its version.

The semantics vectors cover every integer operation a kernel may use (rule 7 of
`tests/wgsl_rules.rs` keeps the kernels inside them), on `i32`, `u32` and two-component
vectors of them where a kernel uses those, with the edge cases above: every pair of a set of values
at and near the edges of both types for the arithmetic, division and remainder, every
shift amount from 0 to 40 and the extremes past it, and bit fields at and past the word.
They mark as unguarded the cases item 4 keeps a module from, a shift amount of 32 or more
and `clamp` bounds out of order, whose result is the hardware's through naga 30.0.1. A run
reports what each adapter gives on them; a difference there does not refuse the adapter,
since no module at this tier reaches one. The file is written from WGSL's definitions by
`wai_ops_gpu::semantics::reference` and checked against it natively, and against the same
reference compiled to wasm32, under Node and in a browser page.

`wai/wai-ops-gpu` meets these conditions. `tests/wgsl_rules.rs` holds every shipped module,
on naga's IR, to items 1, 2 and 4 and to item 3's atomic and barrier parts: the types and
literals; a closed list of expressions and built-in functions; the one atomic operation,
`atomicOr`, its result unused, and no atomic load or store; the bindings, with no workgroup
or private memory and no barrier; the shift masks, no signed left shift, and `clamp`
bounds; that every integer operation a kernel uses is one the semantics suite runs; and
naga's wrapping of signed arithmetic and its guard helpers in each module's MSL and HLSL
translations, which disappear from the MSL with its division checks turned off. Item 3's
other parts, each output word written by one invocation and read by no other, and no
access out of bounds, rest on the host's indexing of the dispatch and on wgpu's bounds
checks, which the checked module-creation call keeps on; the rules check does not decide
them. It also holds the crate to the checked module-creation call, and shows each planted
violation (a floating-point value, `f16`, a subgroup reduction, an `atomicAdd`, an
atomic's result read, an atomic load, an atomic store, workgroup memory and a barrier, a
storage barrier alone, an unmasked shift, a signed left shift, a `clamp` with run-time
bounds) refused by the rule it breaks. Discovery runs item 5's suite and probe on each adapter before it runs a graph;
`tests/semantics.rs` shows every adapter refused when the suite's remainder is written by
hand, `a - (a / b) * b`, which gives `a` where WGSL gives 0. In a browser,
`wai-ops-gpu/browser` runs the same suite through the browser's own WGSL compiler.

## 6. Evidence status

What the repository demonstrates, claim by claim, as of 2026-09-27 (main at
`afc7ece`), except where a row names a later run: the rows that cite run 745 read
the forge's runs of the landing chain's head `7cfcecfa` on 2026-09-30. A test that
skips reports as passing, and a matrix leg whose runner did not run reports
nothing; neither is evidence.

The CI rows below come from the forge's commit statuses
(`/api/v1/repos/Transaction-Science/open-standards/commits/<sha>/statuses`), read
for every commit on main since 2026-08-10 and for the pre-merge commits named, not
from the workflow definitions. Two workflows run the same golden-byte suite
(`wai/byte-exact-conformance`, `cargo test --release`): the *cross-arch
byte-equality* job of `wai-conformance.yml`, and `wai-byte-equality.yml`. Both
declared Linux x86_64, Linux arm64, macOS arm64 and Windows x86_64 legs until
2026-09-30, when the Windows x86_64 legs were taken out of both workflows because its
runner is offline; there is no Windows leg now, and no macOS x86_64 or Windows arm64
leg. No Windows x86_64 job ran for this repository after 2026-08-27: every later
Windows status on main is "Waiting to run" or "Has been cancelled", and the forge's
task list has no Windows task after that day. The Windows results below are history.

| claim | evidence | where |
|---|---|---|
| Cross-architecture identity of the rANS coder, `int_synth`, and the JPEG AI symbol decode (a reference-encoder codestream, given its distribution indices) | golden-byte assertions passed on Linux x86_64, Linux arm64 and macOS arm64 on main at `afc7ece` (2026-09-27, `wai-conformance.yml`; `wai-byte-equality.yml` last ran on main at `3f50097`, 2026-08-29, all three legs passing), and on Windows x86_64 last on main at `3e7811f` (2026-08-27). Between `3e7811f` and `afc7ece` the suite only gained the video and keyframe goldens; these goldens, the sources they decode and the suite's lockfile are unchanged | `byte-exact-conformance/`; `.github/workflows/wai-conformance.yml`, `wai-byte-equality.yml` |
| Cross-architecture identity of integer inter-frame video, and of `int_hyper` on a small learned keyframe | the same suite, passing on Linux x86_64, Linux arm64 and macOS arm64 on main at `afc7ece` (2026-09-27). Windows x86_64 never decoded the keyframe clip: it was added in `d4c922b` (2026-08-28), whose Windows legs were cancelled. Windows decoded the integer video goldens only on the pre-merge commits `c9be2ad`, `256e5dd`, `7dfca26` and `3d34860` (2026-08-25/26), whose video sources, vectors and golden file match main but whose `byte-exact-conformance/Cargo.lock` differs; the change reached main at `3f50097`, after the last Windows run on main | as above |
| `wai.image.jpegai` exact decoder: the integer derivation of the distribution indices matches the reference decoder exactly | a test on one machine that skips unless its fixtures (sink-supplied weights, normative tables, codestream) are provided; CI does not provide them | `wai-rs/tests/jpegai_hyperscale_calibration.rs` |
| `wai.image.jpegai` exact decoder, whole decode through the one public `decode()` call: it stays within a stated bound of the reference decoder's output (max\|Δ\| ≤ 2, ≤ 5% of samples); two decodes in one process are byte-identical, and a reconstruction binding taken over one verifies the other | `wai-jpegai-e2e.yml` fetches the pinned `jpegai/v1` inputs (an extraction of the reference-software assets, published under WAI's models root), checks each file against the SHA-256 manifest `tools/jpegai-e2e-v1.sha256`, and runs both tests with `WAI_REQUIRE_SOTA_VECTORS=1`, under which a missing input fails instead of skipping. The job also fails on any skip banner in the test log, and requires the decoded pixels to hash to a pinned SHA-256. One 256×256 codestream. The workflow has run on the forge's Linux runner (x86_64 hardware) and passed on every run since 2026-09-28. Before that, its job passed under `forgejo-runner exec` on the same host (2026-09-27) with the exact decoder as it stood on main at `afc7ece`, which calls the platform `exp`; there too the decoded pixels matched the pinned hash, the value measured on macOS arm64 | `wai-rs/tests/jpegai_e2e_full.rs`, `jpegai_reconstruction_binding.rs`; `.github/workflows/wai-jpegai-e2e.yml` |
| `wai.image.jpegai` exact decoder across platforms: the `jpegai/v1` codestream decodes to the same RGB8 bytes, and to the same bits in the float values the decoder reports after each stage (values inside a stage are not hashed), on each platform measured | measured 2026-09-28 with rustc 1.98.0, release builds, inputs checked against their SHA-256, all four on the source of the change that added this row: macOS arm64 (native, on the development machine); wasm32-wasip2 (wasmtime 49, on the same machine); Linux arm64 (glibc 2.36, a container in an arm64 Linux VM on that machine); Linux x86_64 (glibc 2.36, the x86_64 build run in that container under user-mode emulation, QEMU 7.0, not on x86_64 hardware). All nine pinned hashes matched on each platform. On all four they also matched on an earlier state of that change, before the decode's `exp` and rounding moved to `float_repro`. Before the move the decode called the platform `exp` on every target, and its roundings compiled to calls to the symbols `round`, `roundf` and `rint` on x86_64 and `round` and `roundf` on wasm32, which the link resolves: an x86_64 Linux test executable inspected after the move, whose other code still rounds this way, took them from Rust's compiler builtins, not the C library (for wasm32 this was not checked). Not measured: macOS x86_64 (no x86_64 translation layer is installed on the development machine), Windows, x86_64 hardware, 32-bit targets. The test skips unless the inputs are supplied. `wai-jpegai-e2e.yml` supplies them and requires this test to run and pass on its Linux runner; that workflow has run on the forge's Linux x86_64 runner and passed on every run since 2026-09-28 | `wai-rs/tests/jpegai_cross_platform.rs` |
| `float_repro`, the decode's `exp` and rounding: the same bits over a fixed grid of inputs; `exp` within 2 ULPs of the host math library (1 ULP measured); rounding equal to the standard library's | library unit tests under the library-test step of the conformance workflow (`neural_int`), which has run them on its macOS, Linux x86_64 and Linux arm64 runners since 2026-09-28. The pinned-bits and rounding tests never skip. The host comparison asserts the host library's accuracy as much as this module's. Its gate selects by target, not by host: it runs on macOS arm64, on Linux x86_64 and arm64 with glibc, and on WASI, and is reported as ignored, with the reason, elsewhere, Windows included. On the source of the change that added this row it was run against these hosts' `exp`, with a worst case of 1 ULP on each: the platform math library on macOS arm64; the WASI C library under wasmtime; glibc 2.36 on Linux arm64, and glibc 2.35 in the Ubuntu 22.04 image the workflow's Linux arm64 runner used on 2026-09-28; glibc 2.36 on Linux x86_64 under user-mode emulation, against three of the four `exp` variants glibc chooses among by processor feature (the variant each run used follows from the features glibc's loader reported active): SSE2 (QEMU 7.0, whose emulated processor reports none of AVX, FMA, AVX2 and FMA4), AVX (QEMU 7.2, `-cpu max` with FMA and AVX2 masked by glibc's `hwcaps` tunable, which leaves AVX active) and FMA (QEMU 7.2, `-cpu max`). On a separate probe of 20 million inputs, the SSE2 and AVX variants returned the same bits, and the FMA variant different bits for some of them. Not compared: the FMA4 variant, which glibc chooses on a processor that has FMA4 but not both FMA and AVX2, and which neither emulator offers. Not run on x86_64 hardware or on other glibc versions, where the gate still runs it; a failure there would say that the host's `exp` and this one are more than 2 ULPs apart, not that `float_repro`'s bits changed | `wai-rs/src/float_repro.rs` tests |
| The exact decode path calls no platform math library | two checks. Clippy's `disallowed_methods` (`wai-rs/clippy.toml`), denied in each module on the decode path, rejects any use of a listed float method, including `f64::exp(x)` and `.map(f64::exp)`. `tests/jpegai_decode_symbols.rs` lists the symbols of an executable that links only the decoder's entry points and weight loaders. It rejects any C math-library function the executable imports, and any it defines other than the round-to-integral functions (`floor`, `round`, `rint` and the like), whose results IEEE 754 fixes. It allows those definitions because a definition shows what the linker kept, not what the decode calls: linked with GNU ld 2.40 for x86_64 Linux, the debug executable defined the compiler builtins' `floor`, though nothing in it referred to `floor`. On the source of the change that added this row, both checks pass on macOS arm64, the symbol check in debug and release builds. The symbol check also passes, in debug builds, on Linux arm64 (the container above) and on Linux x86_64 under user-mode emulation, linked with GNU ld (reporting that `floor` as allowed) and with LLD. With a platform `exp` put back as `f64::exp(…)`, both fail on macOS arm64 while the decoder's unit tests still pass, and the symbol check fails on Linux x86_64 with GNU ld. With a float `%` put into the decode, the symbol check fails on macOS arm64 and clippy, which does not see operators, passes. The conformance workflow runs both on its unix legs, on the forge's runners since 2026-09-28 | `wai-rs/clippy.toml`, `wai-rs/tests/jpegai_decode_symbols.rs`, `.github/workflows/wai-conformance.yml` |
| `wai.image.jpegai` scope: the reference codestream is accepted; copies with β, `model_id`, the colour transform or an LSBS flag changed are refused; a flush-to-zero or round-toward-zero environment is detected. The float ONNX path calls the same check | library unit tests over a committed copy of the reference codestream, which never skip and fall under the library-test step of the conformance workflow (`neural_int`); the environment test runs on arm64 only. Run on the four platforms above, on the source of the change that added the cross-platform row (the environment test on macOS arm64 and Linux arm64). The float path's refusal (β and `model_id` edits) is tested only with its ONNX bundle supplied (`jpegai_scope_ort.rs`, skips otherwise) | `wai-rs/src/jpegai_decode.rs` tests, `wai-rs/tests/vectors/jpegai/` |
| SPEC §7's replicate rule: the registry class decides first, so an integer-declared prior over `wai.image.jpegai` or a float codec is refused; `wai.image.jpegai` is the only `NeuralEntropyExact` capability; the standard codecs sit in the classes SPEC §7 lists, and the tolerance and unclassified ones are not refused; an encoder that pins no prior (`wai wrap`) writes `replicate` only over the standard classes, for the capability and for any declared fallback, or, in the `WAI2` companion form, for the base payload's capability and each companion's | library unit tests (`codecs`, `container`) that never skip and fall under the same library-test step. The strict-decode test in `tests/neural_native_decode.rs` needs the `neural` feature; `wai-neural-gates.yml` runs it on the Linux runner, in an Ubuntu 24.04 image because the `neural` test binary does not link in that runner's default 22.04 image. Its strict-decode job ran on the forge and passed on every run from 2026-09-28; before that the job passed under `forgejo-runner exec` on the Linux runner's host (Linux x86_64, 2026-09-27). The pin-aware tests since added to that workflow (its longer exact list of `neural_native_decode` tests, the step of model-free `neural` library tests, and the neural C ABI step, `tests/ffi_neural_pin.rs`) have no recorded forge run yet. The `wai` binary no longer needs the `codecs` feature, so the tests that run `wai wrap` (`cli_wrap_intent.rs`, and `cli_registry.rs` for the candidate refusal and the registry export) run in the conformance workflow's carriage step on every leg; this row records no forge run of them yet. The C envelope packers emit through `Wai::to_emit_bytes` and `WaiMulti::to_emit_bytes`, whose candidate refusal a library test checks with no feature (`the_emit_path_refuses_a_candidate`, in the library-test step); the C wrappers themselves build under the codec-free `ffi` feature, and `tests/ffi_envelope.rs` (`envelope_pack_refuses_a_candidate`, `envelope_select_never_picks_a_candidate`) runs in the conformance workflow's C ABI envelope step on every leg; this row records no forge run of that step yet. The browser writers' refusal runs in `wai-web-wasm.yml` (`verify_pack.mjs`) | `wai-rs/src/codecs/mod.rs`, `container.rs` tests; `wai-rs/tests/neural_native_decode.rs`, `cli_wrap_intent.rs`, `cli_registry.rs`; `wai-web/demo/verify_pack.mjs` |
| `wai.neural.int_hyper` and `wai.video.int_hyper` at full model scale (the pretrained `mbt2018_mean`, exported to integers): the Rust decode reproduces the export tool's integer reference byte for byte (synthesis, hyper-decoder, the whole conditional bitstream, an eight-frame clip), and costs at most 1 dB against the same model's float decode on one pinned photograph | `wai-sota-vectors.yml` regenerates the vectors on the runner from pinned inputs (the photograph's pixels and the checkpoint's bytes by SHA-256, the Python stack by version) and runs both tests with `WAI_REQUIRE_SOTA_VECTORS=1`, under which a missing vector fails instead of skipping. The job also fails on any skip banner in the test log, and runs `require_vectors_rule.rs`, which checks that without the switch a variable naming absent or incomplete inputs fails as well. Rust is checked against the export of the same run on the same machine: the vectors come from float computation, and some of them differed between machines and between runs, so none of their hashes is pinned and none is compared across machines. The workflow has run on the forge's Linux runner (x86_64 hardware) and passed on every run since 2026-09-28 | `wai-rs/tests/int_hyper_synth.rs`, `int_video_sota_keyframe.rs`, `require_vectors_rule.rs`; `.github/workflows/wai-sota-vectors.yml` |
| `wai.audio.opus.dred.int`: the profile's features are an exact integer function of the payload and the pinned weights, the same on the CPU and on a GPU, and within the draft's tolerance of its reference features | the corpus `live-conformance/dred/` regenerates byte for byte, and its second implementation (`dred_verify.py`, standard-library Python written from the extension) recomputes every digest and row; the two graphs on the GPU at `acc64` give the interpreter's integers graph by graph and the CPU decode's bytes; on the draft's eight test vectors the features pass the draft's feature test and lie at `total` 0.0021 from the network in floating point (opus-dred §6) | measured 2026-10-22 with rustc 1.98.0 on macOS arm64 (the build host: the wai-rs tests in release, the GPU test on its Metal adapter `gpu-metal-0`) and the Python verifier on a second macOS arm64 host. Not measured: x86_64, Linux, Windows, wasm. CI runs the corpus, the measurement and the verifier on the forge's Linux arm64 runner (`wai-conformance.yml`) and the GPU test on the `macos` runner (`wai-ops-gpu.yml`), with no forge run recorded yet. The weights are fetched by digest, not committed | `wai-rs/tests/dred_exact.rs`, `wai-rs/tests/dred_exact_measure.rs`, `wai-rs/tests/dred_exact_gpu.rs`, `live-conformance/dred_verify.py` |
| `wai.audio.int_codec`, `wai.splat.int_codec` | committed vectors that never skip. Their frozen hashes are the output of an independent Python integer reference, computed when the vectors were exported (`tools/wai_int_audio_export.py`, `tools/wai_int_splat_export.py`); no Python runs in CI. The conformance workflow's fast-kernel steps enable `audio_int` and `splat_int` and, in debug and release builds, decode both vectors against those hashes under the reference kernels and every fast-kernel policy (`tests/int_kernels_vectors.rs`); that passed on the forge's Linux x86_64, Linux arm64 and macOS arm64 runners for the landing chain's head `7cfcecfa` (run 745, 2026-09-30). The same steps also name `tests/int_audio.rs` and `tests/int_splat.rs`, which check each vector against the same hash and against its own metadata (sample count; attribute count, tensor size and grid side); Cargo declares each with its feature as a required feature, so a step that names it without that feature fails instead of running nothing. This row records no forge run of those two | `wai-rs/tests/int_audio.rs`, `int_splat.rs`, `int_kernels_vectors.rs`; `.github/workflows/wai-conformance.yml` |
| `wai.neural.int_mlicpp` on its committed vector (synthetic seeded weights; a 64×64 image, 8 latent channels in 4 slices): the decode reproduces the export tool's independent integer reference byte for byte — the RGB, and every scale bucket and symbol of the latent stream in coding order (512 symbols over all 16 buckets of the scale table, 47 of the symbols coded by the rANS escape) — under the reference kernels and under the fast kernels on 1, 2, 3, 5 and all available threads; one integer unit changed in each of four chosen parameters, one on each path that feeds a value (a hyper-decoder weight, an entropy-network weight, a mean-head bias, a synthesis weight), changes the image; a header whose shapes the model cannot produce, or on which a network would form a buffer over the cap, is refused before anything is allocated (`tests/int_mlicpp_preflight.rs`, which counts allocations); damaged payloads are refused or decode, and never panic in an overflow-checked build | measured 2026-09-29 with rustc 1.98.0 on the source of the change that added this row: macOS arm64 native (the wai-rs tests, with overflow checks on and off; the `byte-exact-conformance` golden, release) and wasm32-wasip2 under wasmtime 49.0.1 (the same tests and golden, release; on wasm the fast kernels run single-threaded as portable scalar code, and `usize` is 32-bit). All pinned hashes matched on both. Not measured: x86_64 — the development machine has no x86_64 translation layer installed, and an x86_64-apple-darwin build of the golden compiles but cannot execute there — nor Linux or Windows. The golden runs on every leg of the byte-exact matrix (`wai-conformance.yml`, `wai-byte-equality.yml`) and `tests/int_mlicpp.rs` on every leg of `wai-conformance.yml` in both profiles. Both passed on the forge's Linux x86_64, Linux arm64 and macOS arm64 runners for the landing chain's head `7cfcecfa` (run 745, 2026-09-30); there is no Windows leg. The dispatch through `decode_envelope_strict` (the prior pinning the one-file model bundle, the strict gate) runs in `wai-neural-gates.yml`. The weights are synthetic, so nothing here measures compression quality | `wai-rs/tests/int_mlicpp.rs`, `wai-rs/tests/int_mlicpp_preflight.rs`, `byte-exact-conformance/src/int_mlicpp_golden.rs`, `wai-rs/tests/neural_native_decode.rs`; `tools/wai_int_mlicpp_export.py` |
| `wai-ops/1-i32` graphs on GPUs (`wai/wai-ops-gpu`, §5, *Claiming the tier on a GPU*): every shipped module meets items 1, 2 and 4 and item 3's atomic and barrier parts on naga's IR; each device gives WGSL's result for every guarded case of the integer semantics suite and agrees with 128-bit host arithmetic on every probe case; every graph vector of `ops-conformance/` that the subset covers gives the interpreter's outputs or refusal code, at `acc32` and at `acc64`; the integer decoders, with their graphs on the device, give the CPU decode's bytes or refusal codes on every committed vector and refusal fixture, and every graph the device runs gives the interpreter's outputs | the forge's run 10236 (job 46928) of `wai-ops-gpu.yml`, 2026-10-22, rustc 1.98.0 and wgpu 30.0.1 (naga 30.0.1), release builds, on the kernel text of the change that added *Claiming the tier on a GPU*, on the adapters §5 lists: Metal on macOS 27.0.1 (arm64), and headless Chromium 148 on that GPU; and, on 2026-10-24 at the commit that added `wai/research/ops-gpu-runs.txt` (which holds both runs' report lines), Vulkan on Linux x86_64 with the 0x1002 adapter (Mesa 26.0.8) and llvmpipe, where every test of the crate ran and passed with `WAI_OPS_GPU_REQUIRE=1` and the semantics suite, the probe, the corpus and the mutants gave the figures below with none differing. `tests/wgsl_rules.rs` passed, each planted violation refused by the rule named. On Metal the semantics suite (`tests/semantics.rs`) gave WGSL's result for every one of 254,464 cases, the 25,088 committed and 4,096 SplitMix64 cases of each operation: 231,690 guarded, and the 22,774 unguarded too (a shift amount of 32 or more, `clamp` bounds out of order); the browser leg ran 82,432 cases with none differing, and the committed file reproduced under the reference compiled to wasm32, in the page and under Node 26. The probe (`tests/arith.rs`) agreed on every word of 1,051,757 cases; the corpus (`tests/corpus.rs`) ran 813 vectors (1,104 decodes) on the 32-bit kernels and 1,426 (1,921 decodes) on the 64-bit ones, 212 of the decodes refusals, with no difference; 209 graphs use an operator outside the subset and ran on the CPU, and 95 vectors are refused at load. Eighteen edits of one kernel expression each (`tests/mutants.rs`) were each caught by the corpus, an edited product by the probe, and a remainder written by hand by the semantics suite at discovery. `wai-rs/tests/ops_gpu.rs` decoded the committed vectors and refusal fixtures of the six capabilities §5 names, the world renderer's two vectors and `wai.image.jpegai`'s `jpegai/v1` codestream on Metal, in the forge run, comparing every graph the device ran with the interpreter as well as each decode's bytes; none differed. The browser leg ran the corpus and the semantics suite, not the decoders. Hand runs of this kernel text on Vulkan on Linux x86_64 (PCI vendors 0x10de and 0x8086, and llvmpipe) and on Windows x86_64 (Vulkan and DX12, PCI vendors 0x10de and 0x8086, and the software DX12 adapter) committed no record, and G1's runs of the 0x10de and 0x8086 adapters were of the kernel text before it; none of those is claimed. `wai-rs/tests/ops_gpu.rs` has not run on the Vulkan adapters on this kernel text. `wai-ops-gpu.yml` runs the Metal and browser legs on the `macos` runner, where an absent adapter fails. No other workflow runs a GPU leg of these tests | `wai-ops-gpu/tests/`, `wai-ops-gpu/browser/`, `wai-rs/tests/ops_gpu.rs`, `wgsl-conformance/`; `.github/workflows/wai-ops-gpu.yml` |
| `wai-ops/1-npu` graphs on a neural processor (`wai/wai-ops-npu`, §5): every graph vector of `ops-conformance/` that `npu.jsonl` lists in the subset gives the committed outputs or refusal code, and the interpreter's, with its arithmetic operators on the device | measured 2026-10-22 with rustc 1.98.0, release builds, on the source of the change that added this row, on the device §5 names (one compute core; the tile array's open toolchain 1.4.3 with its core compiler 22.0.0, Linux 7.0 x86_64). The corpus (`tests/corpus.rs`) ran 733 vectors, 1,005 decodes, 32 of them refusals, with no difference; 302 of the vectors move data only. Each operand width (8-, 16- and 32-bit), the requantisation, the elementwise operators and `lut16` ran on the device. The requantisation probe (`tests/probe.rs`) agreed with §2 on 786,432 cases at every shift from 0 to 63. Twelve edits of the kernel (`tests/mutants.rs`) were each caught by the corpus, among them each of the three roundings mapped to a neighbouring mode of the unit, and the shifts of 60 to 63 left to the unit's shift-round step, which 36 vectors catch. Of the 458 graphs that fail the subset by `acc32` alone, 110 gave other results on the device (`tests/width.rs`). `wai-ops-npu.yml` runs these tests on the `linux-amd` runner, which has this device, and fails when it is absent; this row records no forge run of it yet | `wai-ops-npu/tests/`; `ops-conformance/npu.jsonl`; `.github/workflows/wai-ops-npu.yml` |
| `wai.avatar.face` ([avatars](avatars.md) §7.8), at generation-equivalence: the registered generator's frames and a seeded generator's are the same bytes on the CPU (macOS arm64), under `wasm32-wasip2` and with every graph on a GPU (Metal, at `acc64`, every graph compared with the interpreter), and the corpus's second implementation, in Python's integers, gives the same frames | `wai-rs/tests/avatar_face.rs` and `avatar_face_verify.py` (`wai-conformance.yml`); `wai_avatar_face_vectors verify` built for `wasm32-wasip2` under wasmtime (the job `avatar-face-wasip2`); `wai-rs/tests/avatar_face_gpu.rs` (`wai-ops-gpu.yml`, `metal`). Run on the build host, macOS arm64, on 2026-10-22, for the change that registered it; no forge run is recorded here | `wai/live-conformance/avatar-face/` |

So at `entropy-consistency`, WAI's JPEG AI symbol decode is demonstrated across
architectures (x86_64 against arm64) once the distribution indices are given; the
integer derivation of those indices is demonstrated against the reference on one
machine, by a test CI does not run. At `decode-equivalence`, cross-architecture
identity (x86_64 against arm64) is demonstrated for `int_synth`, integer video and
`int_hyper` on committed small-scale vectors; of these, only `int_synth` has also
been decoded on Windows on main. `wai.audio.int_codec` and `wai.splat.int_codec`
decode their committed vectors to the hashes a separate implementation computed, on
Linux x86_64, Linux arm64 and macOS arm64 (run 745): cross-architecture identity
(x86_64 against arm64) against one pinned hash per vector, on small vectors; neither
has been decoded on Windows. For full-scale `int_hyper`, identity rests on
construction, not on a cross-machine comparison of decoded output: CI checks its
decode against the export's integer reference on one machine per run.
`wai.neural.int_mlicpp`'s committed vector decodes to the same pinned bytes on macOS
arm64 and on wasm32-wasip2 (a 32-bit target), measured on the development machine;
it also passed on the byte-exact matrix's Linux x86_64, Linux arm64 and macOS arm64
legs in run 745. On GPUs, the committed vectors of `int_hyper` (and its video keyframe),
`int_codec` audio and splat, `int_synth` and `int_mlicpp` decode with their graphs on
the device to the CPU decode's bytes on Metal, the adapter §5 lists that runs them, after
it gave WGSL's integer semantics on every guarded case, in the `wai-ops-gpu` workflow's
forge run 10236 (job 46928). On a neural processor, only the operator vectors have run
(`wai-ops-npu`); no decoder runs its graphs there yet.

For the JPEG AI exact decoder's pinned reconstruction (§5), identity across
machines rests on construction, and on one comparison: a single codestream on the
four platforms in the table. The construction is integer arithmetic for the integer
stages and, for the float stages, only IEEE-754 basic operations, which are
correctly rounded:

- evaluated single-threaded in the fixed order of the source, each at its own
  precision (true of x86_64, AArch64 and wasm32 targets; not of x87, which the
  exact decoder refuses);
- with no fused or reassociated operations, which Rust does not emit unless the
  source asks for them;
- in the default floating-point environment, which the exact decoder probes before
  decoding (§5): round to nearest even, subnormals neither flushed nor read as zero;
- with no platform math library on the path. The gain scaler needs `exp`, which
  math libraries are not required to round correctly. Rounding to an integer is
  exactly specified, but on some targets it compiles to a call: with rustc 1.98,
  `f64::round`, `f32::round` and `f64::round_ties_even` become calls to the symbols
  `round`, `roundf` and `rint` on x86_64, and the first two do on wasm32, and the
  link decides what answers them (in the x86_64 Linux executable inspected, Rust's
  compiler builtins did, not the C library). The decoder takes both from
  `wai-rs/src/float_repro.rs`, built from basic operations, so their bits are fixed
  by the source. Clippy's `disallowed_methods`, denied in each module on the decode
  path, and a check of the linked decoder's symbols (`tests/jpegai_decode_symbols.rs`)
  keep math-library calls off the path (see the table above).

That the exact decoder's gain scaler equals the reference decoder's rests on margin,
because the two compute it in different arithmetic. The scaler is
`exp((g + β) · log_k / 2⁷)` rounded to 2⁻¹⁰. The exact decoder evaluates it in f64
with `float_repro::exp`, which was measured within 1 ULP of the host math library
on the four platforms. The reference software evaluates it in float32: the
log-domain gain as float32, times `log_k` rounded to float32, then the float32 `exp`
of its tensor library, then rounding half to even. Neither `exp` is guaranteed to be
correctly rounded. The inputs are exactly the normative gain tables, because the exact
decoder refuses β ≠ 0.

`tools/wai_jpegai_gain_exp_margin.py` computes the exact value of both evaluations
with 60-digit decimal arithmetic, over the gain tables exported from the reference
software, and gates on the result at β = 0. It found the following for every entry
of all eight tables:

- the exact value of the decoder's f64 evaluation lies at least 4 × 10¹⁰ f64 ULPs
  from a rounding boundary;
- the exact value of the reference's evaluation, from its float32 argument, lies at
  least 77 float32 ULPs from one;
- both round to the same integer.

So any float32 `exp` within 77 ULPs gives the reference the exact decoder's scaler.
The tool also fails unless the decoder's `log_k` is the value the reference computes.

This does not extend to every β. The tool's `--beta-sweep` option reports, without
gating, all 5118 values of `g + β` that the tables and the 12-bit header field can
produce. At four of them (1115, 1922, 2285 and 2289), the exact value of the
reference's evaluation lies within one float32 ULP of a boundary. There the
reference's own scaler depends on the last bit of its `exp`, so it can differ
between the platforms the reference runs on. At 2285 and 2289 the exact value lies
just above the boundary, a correctly rounded float32 `exp` lands exactly on it, and
rounding half to even then gives the integer below it. At every `g + β`, the exact
decoder's formula gives the scaler that a correctly rounded float32 `exp` gives.
The reference's own expression, evaluated on the development machine (macOS arm64),
also gave the exact decoder's scaler at all 5118; its float32 `exp` there was at
most 0.85 ULP from exact on those inputs. A decoder that implements β would still
have to settle what the reference's scaler is at those four inputs. This one does
not implement β.

## 7. What exactness costs, and what WAI claims about it

### 7.1 The measured record (informative)

[research/determinism-cost.md](../research/determinism-cost.md) measures what
`decode-equivalence` costs one capability, `wai.neural.int_hyper`, against the float model
it is exported from: the pretrained `mbt2018_mean` at all eight published qualities, each
checkpoint pinned by its SHA-256, coded and decoded by the library it is published with, on
the 24 self-test pictures of the pinned reference set. Its readings are in
`research/determinism-cost/record.json`, with the procedure that took them. Every figure
below is followed by a marker naming its place in the record, and
`tools/wai_determinism_cost_verify.py`, which shares no code with the tool that wrote the
record, recomputes each from the readings and checks it, in CI.

- **Rate at equal quality.** Over the 24 pictures, the integer codec needs a median of
  +1.61 %<!--det:summary.bd_rate_percent.psnr.median--> more rate than the float codec for
  the same PSNR, from +0.56 %<!--det:summary.bd_rate_percent.psnr.min--> to
  +2.97 %<!--det:summary.bd_rate_percent.psnr.max--> by picture, and a median of
  +0.95 %<!--det:summary.bd_rate_percent.ms_ssim.median--> for the same MS-SSIM (BD-rate,
  ITU-T VCEG-M33, over the eight qualities).
- **The integer decode, across platforms.** Every payload decoded to the same bytes on
  every platform reached, with every kernel set each runs:
  0<!--det:summary.integer_cross_platform_total.differ--> of
  4608<!--det:summary.integer_cross_platform_total.decodes--> decodes, in
  24<!--det:summary.integer_cross_platform_total.platforms--> runs of a platform and a kernel set, differed
  from the build host's CPU decode, and 0<!--det:summary.integer_cross_platform_total.refused--> were
  refused. The platforms: an arm64 desktop SoC's CPU (all
  cores, one core) and its GPU through Metal; WebAssembly under wasmtime on that machine and
  on an x86_64 one; and two x86_64 Linux hosts, each on its CPU and on its GPU through
  Vulkan (PCI vendors 0x1002 and 0x10de). The kernel sets: the wide kernels, and the narrow
  ones on NEON, AVX2, AVX2 with VNNI, AVX-512 and AVX-512 with VNNI.
- **The float decode, across platforms.** Decoded again in a fresh process on the CPU of
  the machine that encoded them, with one thread and with the library's default thread
  count, the float streams gave the encoder's own bytes in
  192<!--det:summary.float_cross_platform.macos-arm64-cpu-1t.identical--> and
  192<!--det:summary.float_cross_platform.macos-arm64-cpu.identical--> of
  192<!--det:summary.float_cross_platform.macos-arm64-cpu.decodes--> decodes. On every other
  platform the bytes differed, in
  192<!--det:summary.float_cross_platform.macos-arm64-gpu-mps.rgb_differ--> decodes on that
  machine's GPU, 192<!--det:summary.float_cross_platform.linux-x86_64-cpu.rgb_differ--> on
  x86_64 host A's CPU, 192<!--det:summary.float_cross_platform.linux-x86_64-b-cpu.rgb_differ-->
  on host B's CPU and 192<!--det:summary.float_cross_platform.linux-x86_64-b-cuda.rgb_differ-->
  on host B's GPU. In many of them the entropy decode itself diverged, the decoder choosing
  other tables than the encoder from its own float scales: in
  184<!--det:summary.float_cross_platform.macos-arm64-gpu-mps.symbols_differ-->,
  183<!--det:summary.float_cross_platform.linux-x86_64-cpu.symbols_differ-->,
  76<!--det:summary.float_cross_platform.linux-x86_64-b-cpu.symbols_differ--> and
  192<!--det:summary.float_cross_platform.linux-x86_64-b-cuda.symbols_differ--> decodes
  respectively. Against the encoder's own decode, a decode that diverged changed PSNR by a
  median of
  -17.4 dB<!--det:summary.float_cross_platform.linux-x86_64-cpu.delta_psnr_db_where_symbols_differ.median-->
  on host A's CPU and
  -17.4 dB<!--det:summary.float_cross_platform.macos-arm64-gpu-mps.delta_psnr_db_where_symbols_differ.median-->
  on the encoding machine's GPU.
- **Time and energy.** On x86_64 host A, where every timed round read clean against the idle
  brackets around it, a quality-6 decode on its CPU took a median of
  729.6 ms<!--det:summary.timing.linux-int-cpu-q6.ms_per_decode.median--> and
  92.5 J<!--det:summary.timing.linux-int-cpu-q6.rapl.marginal_j_per_decode.median--> of
  marginal package energy for the integer codec, against
  191.5 ms<!--det:summary.timing.linux-float-cpu-q6.ms_per_decode.median--> and
  13.7 J<!--det:summary.timing.linux-float-cpu-q6.rapl.marginal_j_per_decode.median--> for
  the float codec; with the integer decode's graphs on its integrated GPU,
  845.4 ms<!--det:summary.timing.linux-int-gpu-q6.ms_per_decode.median--> and
  93.5 J<!--det:summary.timing.linux-int-gpu-q6.rapl.marginal_j_per_decode.median-->. On
  the narrow kernel sets of the record's §4.6, the same integer decode, giving the same
  bytes, took 144.8<!--det:summary.timing.linux-int-cpu-q6-after.ms_per_decode.median--> ms and
  12.43<!--det:summary.timing.linux-int-cpu-q6-after.rapl.marginal_j_per_decode.median--> J on host A's CPU,
  against 210.7<!--det:summary.timing.linux-float-cpu-q6-again.ms_per_decode.median--> ms and
  14.03<!--det:summary.timing.linux-float-cpu-q6-again.rapl.marginal_j_per_decode.median--> J for the float
  decode measured beside it. On the build host, under heavy load from other work, the
  integer decode's own CPU energy fell to
  6.43<!--det:summary.timing.macos-int-cpu-q6-after.task_j_per_decode.median--> J at quality 6 and stayed
  above the float decode's 1.28<!--det:summary.timing.macos-float-cpu-q6-again.task_j_per_decode.median--> J.

Each figure holds for the pictures, models, platforms and builds the record names, and for
nothing else (§7.2).

Two published records of float decode across devices are cited in §1. Neither is part of
this record. arXiv 2608.00483 found its streams to decode only within the groups of GPUs
its tests established (Table IV). arXiv 2606.28027 found its float reconstruction to diverge
across devices unless it is resynchronised (§3.2), and its 8-bit integer decode not to be
bit-exact across neural processors, for "non-standard implementations of rounding and
multiplication in quantize/dequantize and re-quantization" (Supplement D.1).

What the record does not cover is listed in its §1: `wai.image.jpegai`'s exact decoder
(one configuration, so one rate point and no curve; and the reference set was not coded
with the standard's reference encoder), `wai.neural.int_synth` and `wai.neural.int_mlicpp`
(no float model with a rate axis), a neural processor (no decoder runs its graphs on one,
§5), and the encoder.

### 7.2 What WAI claims, and what it does not

- **The claim.** A capability registered at `decode-equivalence` decodes a payload to the
  same bytes on every conforming sink (§2, §3). The claim rests on the construction (the
  capability's decode is integer arithmetic end to end, §2) and on the evidence of §6. The
  record of §7.1 is evidence of it for the platforms it names; it is not its ground, and a
  platform the record does not name is covered by the construction alone.
- **The decode, not the encode.** Both the integer and the float codec of §7.1 analyse the
  picture in floating point. The tier covers the decode of the bytes a sink receives: the
  same picture encoded on two machines can give two payloads, each of which every
  conforming sink decodes to one picture.
- **Not claimed:**
  - that exactness is free: the record measures what it cost one capability, in rate at
    equal quality, against one float model, on one set of pictures;
  - that an integer decode is faster, or spends less energy, than a float decode of the
    same model: the record measures both on the hosts and builds it names, and no general
    statement follows from them;
  - that a float decode gives other bytes on another platform in general: the record
    counts what it found on the platforms it names, and a float decode that reproduced
    there would still not meet a tier, whose claim is a property of the capability's
    arithmetic and not of the platforms someone tried (§2, §3);
  - anything for a capability, a model, a picture, a platform or a build the record does
    not name.
- **Normative.** A sink, a receipt or a document **MUST NOT** present a figure of the
  record as holding beyond the pictures, models, platforms and builds the record names. A
  receipt's energy figure is a measurement of the decode it describes
  ([energy-measurement](energy-measurement.md) §5) and **MUST NOT** be taken from the
  record. A figure this specification gives for what `decode-equivalence` costs a
  capability, in rate, distortion, time or energy, **MUST** name the measurement it comes
  from (a record committed with its readings and its procedure, or a test CI repeats) and
  the pictures, models, platforms and builds it holds for; a figure taken from a committed
  record is quoted with a marker its verifier checks.