Distinguish main matches from related suggestions and ship 0.1.9
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# New absence holdout, v3
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These 36 queries were frozen before their model results: 16 answerable,
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16 close-but-absent, and four broad/partial requests. Each intent has an English
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and German version. The fixture SHA-256 is in `fixture.lock.json`.
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This is a new query holdout over an existing source corpus, not a new corpus
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holdout. No historical absence query or model output was read to author these
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queries. The author inspected document/code bytes and the original independent
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media descriptions. One AI annotator supplied the labels; independent human
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review has not happened. In particular, audio/music labels rely on the original
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publisher descriptions and tags, not fresh human listening. Report these
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categories separately rather than implying universal ground truth.
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The 36 declared inputs are all 14 `relevance-v2` source files, all 20 licensed
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`media` files, and two owned speech PCM fixtures containing Article 1 of the
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Universal Declaration of Human Rights. See each original source manifest and
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README for license, attribution and derivation. Existing licenses remain in
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force. Speech WAVs remain ignored local test assets; their exact hashes are
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frozen here, without adding another binary copy to Git.
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Before inference:
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```powershell
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python fixtures/absence-v3/verify.py
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```
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Index a dedicated copy of only the declared bytes, preserving basenames.
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Document/code/photo/audio/video extraction failures and missing semantic
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channels must be reported independently of retrieval. An empty result after
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failure is not successful abstention. Record asset coverage before interpreting
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the strict absent metric. The four partial requests are not strict absence
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tests; keeping relevant partial evidence is acceptable.
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The old relevance runner deliberately accepts only the frozen v2 fixture.
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This fixture requires a separate runner or explicit schema adaptation. Do not
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silently bypass its frozen-policy gate or overwrite historical reports.
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`build_fixture.py` records authorship and refuses to overwrite frozen labels.
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Do not rerun it to revise labels after scores become available.
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## Frozen-policy CPU results
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`evaluation-summary.json` records the completed report's hash, runtime/source
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hashes, coverage, timings and per-language/category scores. All 36 assets had
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active semantic channels, with no extraction, semantic or speech failures.
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The 36 queries were evaluated without changing either fixture or policy.
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Primary neighbors appeared for 4/16 absent requests, down from 16/16 under the
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previous leading-cluster rule. The remaining errors ask for a customs export
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declaration when only transit insurance exists, and an HTTP registry fetcher
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when only a local version parser exists. Those related sources exceed the
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display threshold despite lacking the requested facts. Similarity is not
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entailment.
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All 16 positive requests retained their expected sources within five
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accessible results (mean recall@5 1.0). Primary mean recall@5 was 0.625:
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11/16 queries retained at least one expected primary source; 10/16 had the
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correct primary first result. Five positive queries required opening related
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suggestions. Both performer/composition music requests placed the wrong music
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file first in raw retrieval, while retaining the correct file within five.
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This is a real tradeoff, not a claim that all unsupported requests disappear.
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Initial full text/media/speech indexing took 894.9 seconds on the CPU VM.
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Query-role startup took 1.017 seconds; the ordered warm raw query median was
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94.4 ms, maximum 122.9 ms. These are fixture timings, not a general desktop
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performance promise or independent cold-input measurements. The persistent
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profile retains the real index so unchanged files need not be encoded again.
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