Implements sequential evidence and optimal-stopping improvements inspired by the Odds Algorithm.\n\nPhases:\n- Phase 0: fixes anomaly value extraction, sensor false-stability, typed scale thresholds, and enforceable solution gate evaluations.\n- Phase 1: adds immutable decision hypotheses and provenance-aware evidence events with dependence groups and likelihood inputs.\n- Phase 2: adds posterior odds updates, dependence rejection, finite-sequence odds strategy, utility-derived action thresholds, and threshold evaluations.\n- Phase 3: adds sequential experiment analysis with effect estimates, uncertainty bounds, benefit probability, futility, and safety stopping.\n- Phase 4: connects posterior decisions to strategy review tasks and solution gate evaluation.\n\nVerification:\n- Focused suite: 77 passed.\n- Full CI: 109 passed, 0 failed.\n- Migrations through schema 296 applied successfully.
Implements sequential evidence and optimal-stopping improvements inspired by the Odds Algorithm.\n\nPhases:\n- Phase 0: fixes anomaly value extraction, sensor false-stability, typed scale thresholds, and enforceable solution gate evaluations.\n- Phase 1: adds immutable decision hypotheses and provenance-aware evidence events with dependence groups and likelihood inputs.\n- Phase 2: adds posterior odds updates, dependence rejection, finite-sequence odds strategy, utility-derived action thresholds, and threshold evaluations.\n- Phase 3: adds sequential experiment analysis with effect estimates, uncertainty bounds, benefit probability, futility, and safety stopping.\n- Phase 4: connects posterior decisions to strategy review tasks and solution gate evaluation.\n\nVerification:\n- Focused suite: 77 passed.\n- Full CI: 109 passed, 0 failed.\n- Migrations through schema 296 applied successfully.