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feat: Whitepaper benchmarks with CI exclusion and confidence intervals - #2
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Update _compute_stats() to return ci95_lower/ci95_upper/stdev/n using t-distribution CIs for paper-grade statistical rigor. Add _aggregate_multi_run_stats() for cross-run reproducibility evidence.
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Summary
tests/performance/test_whitepaper_benchmarks.py): 21+ benchmarks producing empirical evidence for three paper sections — performance overhead, detection effectiveness, and breach prevention against 2025 MCP attack scenariosOpenAICompatibleClientbase class with vLLM, SGLang, and llama.cpp backend support;create_llm_client()factory for config-driven selection_compute_stats()now returnsci95_lower/ci95_upper/stdev/nvia t-distribution;_aggregate_multi_run_stats()for cross-run reproducibility@pytest.mark.benchmarkare skipped in CI ("not docker and not benchmark")Test plan
pytest -m "not docker and not benchmark" --co -qcollects 0 whitepaper testspytest tests/performance/test_whitepaper_benchmarks.py --co -qcollects 23 testsci95_lower,ci95_upper,stdev,nkeysruff checkand all pre-commit hooks pass🤖 Generated with Claude Code