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chore: import upstream snapshot with attribution
2026-07-13 13:10:45 +08:00

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# Cloud-OpsBench — Fix-A + vocab-fix validation (cheap, gpt-4o, two arms).
#
# Validates two 2026-06-07 fixes in a single paired run:
#
# 1. VOCAB FIX (predictor._ROOT_CAUSES): added 7 performance/admission
# root-cause tokens that were missing, which had pinned unseen-shape a1
# near ~0.01 (object_a1 was ~0.40). Visible on the UNSEEN-shape slice.
#
# 2. FIX A (predictor prompt + adapter._summarize_investigation): make
# opensre's investigation conclusion AUTHORITATIVE for rank-1 instead of
# letting the predictor re-diagnose and discard it. The 2026-06-06 run
# dropped the correct component opensre named from the predictor's top-3
# on 15.2% of opensre+llm failures vs 5.7% for llm_alone. Visible on the
# SEEN-shape slice as the opensre+llm llm_alone contrast.
#
# Two arms so the contrast is directly measurable on the SAME cases:
# opensre+llm — full investigation, conclusion fed to predictor (Fix A)
# llm_alone — same predictor, empty summary (the matched control)
# (llm_alone_pure omitted to halve cost; it is not needed for either fix.)
#
# Run (needs OpenAI network egress — the agent sandbox blocks it):
# cd opensre && set -a && source .env && set +a
# BENCH_MIN_TOOL_CALLS=5 uv run python -m tests.benchmarks._framework.cli \
# run tests/benchmarks/cloudopsbench/configs/cloudopsbench_fixa_validation_openai.yml --dev
#
# Cost: 40 cases x 1 LLM x 3 runs x 2 modes = 240 cells ~= $2.40 (gpt-4o).
# DEV experiment — always --dev. Exploratory, not promotable.
#
# What success looks like:
# - UNSEEN-shape opensre+llm a1 rises from ~0.01 toward ~0.40 (vocab fix).
# - SEEN-shape opensre+llm a1 moves ABOVE llm_alone (Fix A recovers the
# translation loss). On the 2026-06-06 run it was 0.542 vs 0.555 (behind);
# if Fix A works it should pull even or ahead.
benchmark: cloudopsbench
modes:
- opensre+llm
- llm_alone
llms:
- gpt-4o
model_versions:
gpt-4o: gpt-4o-2024-11-20
# 3 runs/case averages single-shot LLM noise; seed pins the SAME case set so
# the opensre+llm vs llm_alone contrast is paired.
runs_per_case: 3
workers: 1
cost_budget_usd: 25.0
seed: 42
output_dir: .bench-results/cloudopsbench_fixa_validation_openai/
pre_registration_path: tests/benchmarks/cloudopsbench/configs/preregistrations/cloudopsbench_v1.yml
filters:
# 40 seeded cases spanning BOTH shape strata so one run validates the vocab
# fix (unseen-shape) and Fix A (seen-shape contrast). Not a publication N.
limit: 40
seen_shape: [true, false]
systems: []
fault_categories: []
report_formats:
- json
- markdown