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. Positions WAI's byte-exact reconstruction (decode-equivalence) as the strongest tier above the entropy-stage guarantee the base learned-codec field provides. No new wire format; a vocabulary + conformance framing over §7. Keywords MUST, MUST NOT, SHOULD, MAY are RFC 2119/8174.

1. Why tiers

Neural decode reproducibility is not binary. A float neural decode is not reproducible at all — round-off diverges across hardware, and one diverging bit corrupts a learned entropy decode. In response the field has converged on making the entropy stage reproducible while leaving reconstruction to pixels implementation-defined. Two facts make this concrete:

• The base standard. JPEG AI (ISO/IEC 6048) defines its normative 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 post-entropy processes MAY diverge. One codestream may legally reconstruct through up to three synthesis transforms.
• The learned-codec field. The mainstream cross-platform fix is explicit parameter transmission — carrying the entropy/scale parameters in the codestream so the entropy decode is consistent across devices without requiring bit-exact arithmetic. Same target: a reproducible entropy stage, a reconstruction that may vary.

WAI supports that guarantee and a stronger one. Naming both lets a capability be honest about which it makes, and lets a receipt or a provenance binding (§reconstruction-binding) demand the tier it needs.

2. The tiers

entropy-consistency

The entropy decode reproduces byte-for-byte across devices — the decoded symbols / quantized latent 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 symbols / quantized latent, NOT the pixels.
• This is the guarantee the base learned-codec standard and the mainstream cross-platform techniques provide. It is the floor for a sound neural replicate under §7: enough that the decode does not catastrophically desync, not enough to hash the rendered result.

decode-equivalence

The whole decode to the final medium reproduces byte-for-byte across devices — the reconstructed pixels (or samples, or attributes) have a portable hash. The sink pins one reconstruction and runs it in integer / fixed-order arithmetic end to end, entropy and synthesis. Where the base codec admits several conformant reconstructions, this tier fixes one.

• Conformance criterion: the medium's *-equivalence (e.g. decode-equivalence for wai.image.jpegai, sample-equivalence for wai.audio.int_codec, splat-equivalence for wai.splat.int_codec).
• This is WAI's contribution above the field's baseline: it is a determinism guarantee beyond what the base standard requires, and it is the precondition for a hard provenance binding over the rendered content — 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.

3. Declaring and using a tier

• A capability's registration (§5) states its tier via its *-equivalence criterion; the WAI capabilities that decode a learned image/audio/3D latent to bytes (wai.image.jpegai, wai.neural.int_*, wai.audio.int_codec, wai.splat.int_codec) are decode-equivalence. A capability MAY instead declare entropy-consistency when it only guarantees the entropy stage.
• replicate-soundness (§7). An envelope with intent = replicate MUST pin a prior that meets at least entropy-consistency; a float neural decode meets neither tier and MUST NOT claim replicate. A receipt that carries a reproducible decoded-medium hash MUST come from a decode-equivalence capability.
• Provenance. The reproducible-reconstruction binding — a C2PA / JPEG-Trust hard binding over decoded pixels — requires decode-equivalence; over an entropy-consistency capability only the entropy-stage output is bindable, not the rendered result.

4. Framing

WAI spans the spectrum rather than competing on one point of it. The entropy-consistency tier is the base learned-codec floor — the guarantee the field and the underlying standards already make — and WAI supports it directly. decode-equivalence is the tier WAI adds on top: pinning one full reconstruction so the rendered result, not only the entropy stream, is reproducible and hashable. Both are stated as capabilities of the standard, not as comparisons.