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Victor KuznetsovandClaude Opus 5
e85307f1c0
The Content Seal manifest named the production GPU worker in its provenance column and in two row keys, several docstrings described their sources as user uploads from the private corpus, and that corpus was called by the storage product holding it. Each is now what a public reader can check: a pipeline and strength, material that is not publication-cleared, and a private corpus. The corpus test follows the renamed rows. Deployment and tier detail goes the same way, since which backend each hosting tier can afford is a product decision, not a library fact. Twelve harnesses that need a cloud GPU account leave scripts/. The research pages that cite their findings name them as harnesses kept outside this repository and stop implying a reader can rerun them; the tracked inputs and recorded verdicts remain the durable record, and the offline analysis step stays here with its test. A development invariant states the rule so the next harness is not added back. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
67 lines
2.9 KiB
Markdown
67 lines
2.9 KiB
Markdown
# Engine-selection fixture set
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This directory is the tracked, content-balanced input set for researching a
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deterministic `auto` engine choice. It is not a training corpus and it is not a
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watermark-oracle corpus.
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## Content matrix
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`content-manifest.csv` contains 19 prompt-matched pairs. Each pair uses the
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same prompt and generation index across OpenAI `gpt-image-1-mini` and Meta
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`muse-image-1.0`, covering the content classes that materially stress image
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regeneration:
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- natural and urban scenes;
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- single-person portraits and people in context;
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- animals, food, products, interiors, architecture, macro detail, and action;
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- flat, isometric, watercolor, and surreal illustration styles.
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The files are exact copies of generation outputs collected for the
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`docs/arxiv-paper-review` research branch. The manifest records the SHA-256 of
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the committed bytes. The old OpenAI collection manifest stored the API payload
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hash under `sha256`; `source_payload_sha256` preserves that value while
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`sha256` always means the actual file bytes here. Meta's two hashes are the
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same.
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The project maintainer has cleared these exact committed outputs for permanent
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public test and benchmark reuse. `reuse_basis` records that clearance on every
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row. It is permission for this fixed fixture set, not a claim that either
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provider grants a general-purpose dataset license for arbitrary generations.
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One generation per prompt and provider is enough for the first paired
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discovery pass. Any policy inferred from it must be challenged against unused
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generation indices from the local research archive before shipping. Generated
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outputs belong outside this directory.
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`carrier-manifest.csv` adds the canonical, already tracked signal carriers to
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the study without copying their bytes. Together the two manifests cover
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ordinary content, text, single faces, face grids, and mixed face/text scenes.
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## Signal carriers
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The content matrix was re-encoded by its collection pipeline and has no local
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provenance signal. It must not be used to set or certify watermark-removal
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strengths. The tracked oracle/carrier inputs remain in their canonical homes:
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- OpenAI and Google SynthID: `data/synthid/originals/` with
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`data/synthid/manifest.csv`;
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- Meta Content Seal: `data/contentseal/originals/` with
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`data/contentseal/manifest.csv`.
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There is no tracked, publication-cleared Microsoft InvisMark carrier yet. The
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three Paint sources used by the earlier calibration are not publication-cleared
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and remain outside git. A Microsoft row may be added only from a newly generated or
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otherwise publication-cleared source, with a pixel-identical metadata-stripped
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control that remains positive in Microsoft's detector.
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## Integrity check
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Run:
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```bash
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uv run python scripts/verify_engine_selection_fixtures.py
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```
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The check validates paths, byte hashes, dimensions, prompt pairing, and the
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declared provider/content matrix without loading any ML model.
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