Provider classes now name the GENERATOR that rendered the pixels, never
the brand. bytedance joins openai/google/muse-image as the shared
ByteDance lineage (Doubao and Jimeng render with one model family;
measured: separate heads cross-fire 32/22 at 3.5x mass, the surgical
union holds 83.9% on the frozen test cell while openai/google/meta/no_ai
stay byte-identical to run1). tc260 stops being a public value: its head
remains as the group veto for the rest of China's generators (without
it, 207/379 China test rows would be falsely named openai or
muse-image), and the loader tolerates provider snapshots that predate
bytedance. The receipt gate moves model-side: stable-name artifact
receipt-gate.npz carries its threshold, head and threshold always load
from one file, and the publish script stages the stable gate from the
weights source. Microsoft stays metadata-only: 144 of its 253 bank rows
name an OpenAI model as their C2PA softwareAgent and none names a
Microsoft renderer.
pre-commit: 1) maintain.sh - exit 1 (uv-secure: lightning 2.6.5 via
trustmark, PYSEC-2026-3624 no fix released; per docs/development.md the
core checks ran separately: ruff clean, pyright 0 errors, 2059 passed
3 skipped); 2) /simplify - single-pass four angles, clean, one noted
skip (gate-test npz staging duplication); 3) docs sync - grepped
bytedance/receipt-gate.npz/provider values over README, docs, skill,
rules: no stale references; 4) CLAUDE.md - improver: no changes,
revise: no additions (learnings live in canonical docs)
The runtime's _weights_dir resolves the Hub snapshot offline through an
inline allow_patterns list, so a deployment that pre-caches the snapshot
for HF_HUB_OFFLINE=1 had to re-list the files by hand and silently
drifted when a release added one: the offline resolution then failed on
a four-fifths-present cache even though every missing piece had its own
fallback. Export the exact runtime request list as one public name, use
it in _weights_dir, and pin the seam with a test that captures the
snapshot_download call. Document the contract in photo-classify.md and
module-internals.md.
The gate head is fitted on the freeze CLIP-L-ft embedding space, so it
belongs with the model: _load_receipt_gate now prefers
receipt-gate-2026-09-02.npz from the Hub snapshot or the
RAIW_CLASSIFY_WEIGHTS directory, with the package asset kept as the
fallback for weights frozen before the gate existed. Card mode of the
publish workflow now uploads the gate alongside the README and the
operating point, and the snapshot allow-pattern includes it.
A DEFINITELY verdict now runs a linear receipt gate on the CLIP-L-ft
vector Model 1 already computes: a hit publishes unknown and skips the
124-d provider pass. Trained on CORD-v2 train (800, CC BY 4.0) plus 200
synthetic capture-style receipts against 2,400 ai_train negatives; the
threshold is certified on CORD validation and a synthetic holdout with
no field-receipt pixels. Field receipts false-AI 19/59 -> 2/59, CORD
89/99 -> 0/99, ai_test DEFINITELY cost 7/1,847.
The Hub model and the private training repository were both called
raiw-models, so the training repo's README had to spend a paragraph telling
the two apart. Renamed on both sides: the Hub model to raiw-photo-classify
and the GitHub repository to raiw-classifier-training.
Done now because it is free now. classify.py landed after v0.36.0 and is in
no release, and the Hub model had zero all-time downloads, so no installed
package and no user carries the old id. Hugging Face redirects the old URL
and preserves counts, and the pinned freeze revision c0ac82b6 resolves on
the new name -- verified by downloading through the library's own constants.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The head was trained on mixed Chinese AIGC producers that share the
TC260 mark. openai and google stay provider classes. muse-image stays
a model class.
openai, google, and tc260 stay provider classes. The Muse Image head
is public as muse-image, not meta. The freeze file still keys that
head meta_muse_image.
Ship Model 1 then gated Model 2 as an explicit CLI and library call.
identify, has_invisible_target, and all do not import it. Weights stay
on Hugging Face wiltodelta/raiw-models at a pinned revision.
Victor Kuznetsov