feat: useful-flow Phi + sensitivity primitives (+ example 08 system-simulation) - #6
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…n both languages Adds the cmg-native simulation capability the Tower simulations surfaced: score configurations by useful flow (Phi = quality/cost) and rank knobs by sensitivity (what to tune next). Configuration what-if + sensitivity is cmg's lane; flow-network min-cut stays in intelligence-flow. - core: usefulFlowScore + combineCost/combineQuality (TS + Python), beside compareRuns. - math: sensitivity + rankSensitivity, finite-difference gradient (TS + Python), beside errorSensitivity. - example 08-system-simulation in both languages: sweep a parameterized pipeline, score by Phi, pick the best, rank knobs by sensitivity. TS and Python output is byte-identical; each has a self-check + expected-output.txt. - docs/05-useful-flow-and-sensitivity.md: the math/principle (network flow for Phi, finite-difference gradient for sensitivity), framed for math / engineering / CS. - CHANGELOG entries (Change / Why / Domain influence / Languages). No modelgraph changes (cmg is standalone). Parity enforced. Domain-free. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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What
Adds the two simulation-derived primitives to both languages, plus a domain-free example that exercises them.
usefulFlowScore(core) - Phi = Q / C, the useful-flow-to-an-accepted-sink score (network flow / OR), withcombineCost/combineQualityhelpers.sensitivity/rankSensitivity(math) - finite-difference d(objective)/d(knob); ranking knobs answers "what to tune next".Parity
Primitives ship in TypeScript and Python; example 08 output is byte-identical across both (
expected-output.txt).Verify
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