48 lines
2.9 KiB
Markdown
48 lines
2.9 KiB
Markdown
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# Relevance calibration preparation
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The existing frozen mixed diagnostic returned candidates for all ten requests
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without a relevant source. That observation identifies an abstention problem;
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it does not justify choosing a cosine cutoff on those same queries.
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The new [text/code diagnostic](../fixtures/relevance-v2/README.md) separates
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calibration and held-out topic families and repositories before inference.
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Its 14 files support 48 English/German queries: 32 positive requests and 16
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requests for absent artifacts or implementations. Both translations of an
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intent stay in the same split. A broadly related neighbour may be useful for
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an exploratory topic query, so these absence labels require concrete artifacts.
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The source inventory and blinded review have been completed. Independent AI
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review agreed with all expected file sets; the final labels are frozen in
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`fixture.json` with hashes and a UTC timestamp in `fixture.lock.json`.
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No retrieval run, threshold selection or ranking change has occurred. Agent
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agreement will not be described as human review.
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The first independent agent review detected Windows-1252 mojibake in the
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authored German requests and ambiguity in the wildcard-comparator request.
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Before inference or freezing, German request strings were restored to Unicode
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and the comparator request clarified to a lone `*` followed by another
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comparator, such as `*, >=1.0.0`. Numeric partial versions such as `1.*` are
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outside that rejection claim. The original blinded input and per-query changes
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are preserved in `fixtures/relevance-v2/review-history`; upstream source and
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licence bytes were unchanged. The corrected independent re-read agreed with
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all expected file sets. Before freezing, two further paired requests were made
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more precise: bounded pattern search requests its first match, and the museum
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request asks for operative loan terms rather than an executed agreement.
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Both the preceding input and report remain byte-exact in review history;
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independent re-reading confirmed those four wording changes.
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An absent label means the requested artifact or implementation is missing.
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It does not mean no related file exists or that related files must never be
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shown as suggestions. Any future abstention policy must distinguish that
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semantics explicitly when it reports results.
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After freezing, use calibration only to propose an explicit relevance policy;
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**Keep held-out queries unevaluated until that policy and its parameters are fixed.**
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report positive recall alongside absent-query behaviour. Preserve raw candidates,
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native and end-to-end timings, model/runtime hashes, task scope and any unsupported
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filters. Evaluate the held-out split once without selecting parameters from it.
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If labels need correction after model output, create a new version and explain
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why instead of altering this diagnostic silently.
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This small corpus does not close independent human relevance, media abstention,
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music discrimination, speech localisation or production-scale quality gates.
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