glm-5.1 7bfcfecc7a feat(cost-benefit/dag-propagation): implement DAG-propagation effective probability computation ...

Implement computeEffectiveP internal helper and workflowCost public function
that captures the structural reality that upstream failures multiply downstream
damage, per ADR-004 and the Python research model.

- computeEffectiveP: computes pEffective from intrinsic probability + upstream
  propagation using inherited quality factors (parentP + (1-parentP) × qualityRetention)
- workflowCost: processes tasks in topological order, computes per-task EV with
  degraded effective probability, includes pIntrinsic/pEffective split
- Supports independent and dag-propagate modes
- Completed tasks excluded from results but propagate p=1.0 when includeCompleted: false
- Per-edge qualityRetention overrides defaultQualityRetention option
- Throws CircularDependencyError for cyclic graphs via topologicalOrder
- 30+ new tests covering chain compounding, diamond graph, mode comparison,
  completed task semantics, cycle detection, per-edge qualityRetention

2026-04-27 12:52:47 +00:00

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