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open-standards/wai/ops-conformance/certificates/wai.neural.int_hyper/3b3b56053917bf0b4a49b7011f4c725e3d413da865caec2b6bd57b953c115fb2.json
Transaction Science 3f9c3ef640 wai.neural.int_hyper and wai.neural.int_mlicpp: natural, sets trained on openly licensed natural pictures, held to one decode natively, through their lowerings, in WebAssembly and on GPUs 
int_hyper `natural` (wai-rs/tests/vectors/int_hyper/natural_*, record natural_set.json): a
mean-scale hyperprior, 64 synthesis and 64 latent channels, trained by
tools/wai_int_hyper_train.py at 661048492 from seed 20261112 on the 599 training pictures of
research/training-pictures/natural-1.json in 256 x 256 crops: 32,000 steps of 8, Adam 5e-4 then
5e-5 for the last quarter, lambda 0.0130, clip 1, deterministic algorithms, on the Linux x86_64
build host's integrated GPU (PCI 0x1002, gfx1151) through ROCm 7.2 and torch 2.14.0+rocm7.2, in
90 min (03:21:58-04:52:14Z); last logged step 0.5152 bpp at 26.23 dB (loss 2.53). Listing (its
identity) 3b3b56053917bf0b4a49b7011f4c725e3d413da865caec2b6bd57b953c115fb2. Coded picture: the
dead-leaves picture of that seed, stream 0, 128 x 128: 1,540 rANS bytes, 0.752 bpp, 18.74 dB,
decode 08b6bf5b...; WIH1 7922be10....

int_mlicpp `natural` (wai-rs/tests/vectors/int_mlicpp_natural/, record natural_set.json): 64
channels in 4 slices, a 48-channel hyper-latent, a 3 x 3 entropy network, trained by
tools/wai_int_mlicpp_train.py at 661048492 by the same recipe side by side on the same GPU, in 99
min (to 05:01:24Z); last logged loss 2.5833 (0.5009 bpp, 26.08 dB). model.wmb
27643187da6ba44cd5c8e3fc67ff4cf7e26026942414965cfcbd3a4b18bd4285 (1,988,549 B), listing
f37e2302bb2b26bb9a5483bf1f17e0210ecc2cf88b2b55e8b0aaaa6f4e79964b, prior id int_mlicpp/natural.
Coded picture: 1,512 rANS bytes, 0.7383 bpp, 18.65 dB, decode 1bbd9de6.... The synthetic vector
is unchanged under its own identity.

Both records read Open (check --open: tools at their blobs of 661048492, the manifest by its
digest under the licence rule); self-test 16 of 16 (int_hyper) and 14 of 14 (int_mlicpp).

Held: int-payload-conformance `natural` (WIH1); byte-exact goldens wih1_natural_set_is_golden and
int_mlicpp_golden::natural (by the sink's decode and through the lowering, reference and fast
kernels); wai-ops goldens (stage operators); ops-conformance certificates, both acc64, first
acc32 failure h_s input range at 43 bits, listed after the synthetic sets (committed_after) so
the index only gains entries; module corpus 123 cases; the browser crate's Node check; the GPU
capability test (ops_gpu.rs) with both mlicpp vectors. verifiers/trained-sets.sh runs the
manifest's check and self-test and both records' check --open and self-test.

Run through rb, macOS x86_64 (iMac): gens; wai_int_payload_vectors verify, wai-verify vectors
verify, wai_ops_check.py, int_payload_verify.py, corpus.py --check, all 0; wai-rs tests
int_hyper_ops, int_kernels_vectors, corpus_provenance, int_payload, int_mlicpp, int_mlicpp_ops
(release, no-fail-fast) and int_hyper_ops, int_payload (debug) 0; byte-exact-conformance
(release) 0; wai-ops tests 0; clippy -D warnings clean for neural_int and for the ops_gpu set
(no new lint in any other set: the union set's and byte-exact's and wai-ops's are pre-existing,
none in a touched line). Linux x86_64 (Framework): ops_gpu on gpu-vulkan-1002 and
swgpu-vulkan-10005, int_hyper 15 cases and int_mlicpp 2, graphs_differ 0, errors 0; the 48
test-picture payloads decode by wai_det_cost to the trainers' recorded digests.
Plants caught: the fast kernels' IGDN one off on 64-channel layers fails both trained sets'
goldens (3 tests) while 116 pass; one byte changed in leaves_g_s.bin and in int_mlicpp/model.wmb
(changed bytes under an existing set's name) each fail `wai breaking` as an unacknowledged wire
break; wai breaking against 88b0bdb4c passes (25 compatible changes).

Co-Authored-By: Claude Opus 5.5 <[email protected]>
2026-11-12 01:35:09 -05:00

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