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Automated release from CI pipeline

Changes: feat(swarm): ADR-149 evaluation harness — GDOP, IQM+bootstrap CI, noise sweep (#875)

Stage-1 kinematic evaluator per ADR-149 (peer-reviewed). Pure Rust, no new deps.

evals/:

  • gdop.rs: 2D Geometric Dilution of Precision ((HᵀH)⁻¹ trace-sqrt); None for <2 observers or collinear/singular geometry
  • stats.rs: IQM (Agarwal 2021) + 95% stratified-bootstrap CI (deterministic LCG)
  • probability_of_improvement
  • metrics.rs: EpisodeMetrics + AggregateMetrics::from_strata (IQM±CI, seed-stratified)
  • runner.rs: seeded kinematic rollout (FlightPattern-driven), seed×episode matrix, 3σ×3κ default noise sweep (Gaussian amplitude × von Mises phase)
  • report.rs + eval_swarm bin: generates evals/RESULTS.md leaderboard

RESULTS.md surfaces the real coverage-vs-localization-precision trade-off via GDOP: partitioned wins coverage (100%) but single-drone sightings (GDOP 0 → 7.0m); pheromone gets multistatic fusion (GDOP 1.6 → 4.1m). Wi2SAR 5m paper-baseline row included.

Stage-2 (Gazebo/PX4 SITL false-alarm + collision on median seeds) is documented follow-on.

Tests: 116 default / 133 full+train (+13 eval tests), 0 failed. Clippy clean (-D warnings).

Docker Image: ghcr.io/ruvnet/RuView:8d64434d21e5fe25cac33bbd6331d9e5a8cc05f0

Source: README.md, updated 2026-05-30