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Fix reference artifact reproducibility in CI
1 parent a2317db commit d3c7edd

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Lines changed: 8 additions & 1 deletion

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.github/workflows/harness-ci.yml

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@@ -30,6 +30,7 @@ jobs:
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--config benchmarks/configs/default.json \
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--system-name "Reference Example System" \
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--system-version "1.0.0" \
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--date-evaluated "2026-04-03" \
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--observations benchmarks/fixtures/reference_observations.json \
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--output /tmp/reference-example-v1.json
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benchmarks/fixtures/reference_observations.json

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@@ -94,12 +94,14 @@
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"Reference example generated from the documented v1 fixture set.",
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"Suitable as a schema example and smoke test for the v1 scaffold."
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],
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"artifact_class": "reference_example",
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"caveats": [
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"This is a reference example artifact, not a competitive public submission.",
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"Some scenario evidence is summarized rather than linked to raw transcripts."
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],
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"evidence": [
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"benchmarks/fixtures/reference_observations.json",
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"benchmarks/configs/default.json"
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]
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],
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"confidence_level": "low"
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}

eval/runner.py

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@@ -51,6 +51,8 @@ def main() -> None:
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evaluator_notes = observations.get("evaluator_notes", [])
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caveats = observations.get("caveats", [])
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evidence = observations.get("evidence", [])
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artifact_class = observations.get("artifact_class")
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confidence_level = observations.get("confidence_level")
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metric_weights = config.get("metric_weights", {})
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metric_scores = build_metric_scores(metric_weights, scenario_scores, metric_overrides)
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scenario_coverage = round(
@@ -74,6 +76,7 @@ def main() -> None:
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"Benchmarking Persistent AI and Digital Twin as a Service Systems",
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),
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"artifact_type": "benchmark_result",
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"artifact_class": artifact_class,
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"system_name": args.system_name,
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"system_version": args.system_version,
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"date_evaluated": args.date_evaluated,
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"evaluator_notes": evaluator_notes,
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"caveats": caveats,
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"evidence": evidence,
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"confidence_level": confidence_level,
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}
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write_json(args.output, payload)

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