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Adding Microsoft SECURITY.MD#2

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Feb 27, 2026
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Adding Microsoft SECURITY.MD#2
timenick merged 1 commit into
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Please accept this contribution adding the standard Microsoft SECURITY.MD 🔒 file to help the community understand the security policy and how to safely report security issues. GitHub uses the presence of this file to light-up security reminders and a link to the file. This pull request commits the latest official SECURITY.MD file from https://github.com/microsoft/repo-templates/blob/main/shared/SECURITY.md.

Microsoft teams can learn more about this effort and share feedback within the open source guidance available internally.

@timenick timenick merged commit aefc856 into main Feb 27, 2026
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@timenick timenick deleted the users/GitHubPolicyService/6eb8e284-81b0-4daf-a82d-73989620f602 branch February 27, 2026 07:50
DingmaomaoBJTU pushed a commit that referenced this pull request Jun 22, 2026
…sampling, promote_findings)

Aligns the autoconfig POC code with docs/self-evolution-design.html section 4. Implements
the components still marked TODO; champion-config, arch pruning (build_insight) and
the feature-gaps log were already done.

bench_utils.py (Fix #1/#2/#5):
- run_perf_session: atomic single-session perf primitive
- paired_ab_bench / adaptive_paired_ab_bench: interleaved baseline-vs-hypothesis
  A/B so DVFS drift cancels in the within-pair ratio; adaptive variant samples until
  the 95% CI is decisive (KEEP/DISCARD band) or MAX_PAIRS, returns verdict + CI
- thermal_classify: COOL/WARM/HOT_RUN from a cold reference latency
- session_cv: shared between-session noise-floor helper

promote_findings.py (Fix #4, NEW): reads catalog-*-sweep/*/results.json, applies the
L1->L4 confidence ladder (L2 = effect-size gate, L3 = >=2 models/arch, L4 = >=3 arch
classes), writes ep_knowledge/_auto_promoted.json as a draft sink that never clobbers
curated KB. Tolerant of both QNN (full.p50s_ms) and GPU/CPU (full_p50s_ms) schemas.

catalog_qnn_sweep.py (Fix #1 wiring): opt-in --paired-ab flag (default off) that runs
adaptive paired A/B per hypothesis against the baseline ONNX and records verdict + CI;
sequential Phase B remains the default path.

README: document the self-evolution tooling and refresh the directory layout.
DingmaomaoBJTU pushed a commit that referenced this pull request Jun 26, 2026
…gistry, non-mutating model_type override

- registry: replace decorator/register API with a plain QUANT_FINALIZERS dict; get_quant_finalizer lazily imports + instantiates (review #1).
- quantizer: resolve+apply the model-type-specific quant policy inside quantize_onnx from config.model_type, a single seam shared by all callers; drop the duplicated dispatch blocks in commands/build.py and build/hf.py (review #2).
- loader: thread an explicit model_type as model_type_override through resolve_task instead of mutating hf_config.model_type, so exporters/patchers keep the architecture's native type while the loader config surfaces the build variant (review #4).
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