V1.1.0 — Performance decision engine

V1.1.0 adds an evidence-gated performance decision engine for CUDA, CUTLASS, Triton, and complete GPU workloads.

Highlights:

• Builds a deterministic critical-path performance model from admitted evidence.
• Keeps active diagnosis to three competing mechanisms and one lowest-cost next action.
• Produces an early investment brief before expensive profiling.
• Returns MEASURE, PURSUE, REVIEW_REQUIRED, or STOP from explicit evidence and project thresholds.
• Prevents closed mechanisms from being reopened through renamed hypotheses or overlapping scopes without fresh, relevant evidence.
• Records external AI challenges without treating unrelated workload evidence as an answer.
• Adds four RTX 5090 Controller evidence-admission scenarios and a separately authorized disposable NCU smoke path.

Validation:

• 1,190 automated tests: 1,180 passed and 10 physical-GPU opt-in tests skipped in the CPU/static suite.
• Python 3.10 and 3.12 CI passed for commit 095c872.
• The physical RTX 5090 V1.1 lane passed 24 of 24 checks; the final four hardened Controller scenarios were rerun successfully.

V1.1.0 为 CUDA、CUTLASS、Triton 与完整 GPU workload 增加了基于证据的性能决策引擎。它会先建立关键路径模型，再从少量竞争机制中选择最低成本的下一步；在进入昂贵 profile 前给出投入判断，并阻止已关闭方向通过改名或调整范围重复消耗优化轮次。