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).

v2/crates/ruview-swarm/src/bin/eval_swarm.rs

@@ -0,0 +1,104 @@
+//! ADR-149 Stage-1 evaluation CLI.
+//!
+//! Runs the kinematic eval matrix over every flight pattern (default) and
+//! writes a ranked `RESULTS.md` leaderboard. Pure Rust — no special feature
+//! flag required, so it builds and runs in default CI.
+//!
+//! Defaults are intentionally small (10 seeds × 10 episodes) so the run is fast.
+//! The full ADR-149 reporting configuration is 10 seeds × 50 episodes — pass
+//! `--seeds 10 --episodes 50` for the publication run.
+//!
+//! ```text
+//! cargo run -p ruview-swarm --bin eval_swarm -- \
+//!   --seeds 10 --episodes 10 --out crates/ruview-swarm/evals/RESULTS.md
+//! ```
+
+use std::path::PathBuf;
+
+use ruview_swarm::evals::metrics::AggregateMetrics;
+use ruview_swarm::evals::report::render_results_md;
+use ruview_swarm::evals::runner::{run_matrix, EvalConfig};
+use ruview_swarm::planning::patterns::FlightPattern;
+
+fn main() {
+    let args: Vec<String> = std::env::args().collect();
+    let mut seeds = 10usize;
+    let mut episodes = 10usize;
+    let mut out = PathBuf::from("crates/ruview-swarm/evals/RESULTS.md");
+
+    let mut i = 1;
+    while i < args.len() {
+        match args[i].as_str() {
+            "--seeds" => {
+                i += 1;
+                seeds = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(seeds);
+            }
+            "--episodes" => {
+                i += 1;
+                episodes = args.get(i).and_then(|s| s.parse().ok()).unwrap_or(episodes);
+            }
+            "--out" => {
+                i += 1;
+                if let Some(p) = args.get(i) {
+                    out = PathBuf::from(p);
+                }
+            }
+            "--help" | "-h" => {
+                eprintln!(
+                    "eval_swarm — ADR-149 Stage-1 kinematic evaluator\n\
+                     Usage: eval_swarm [--seeds N] [--episodes M] [--out PATH]\n\
+                     Defaults: --seeds 10 --episodes 10 --out crates/ruview-swarm/evals/RESULTS.md"
+                );
+                return;
+            }
+            other => {
+                eprintln!("warning: ignoring unknown argument '{other}'");
+            }
+        }
+        i += 1;
+    }
+
+    eprintln!(
+        "Running ADR-149 Stage-1 eval: {seeds} seeds × {episodes} episodes \
+         over {} flight patterns...",
+        FlightPattern::all().len()
+    );
+
+    let mut rows: Vec<(String, AggregateMetrics)> = Vec::new();
+    for (idx, pattern) in FlightPattern::all().into_iter().enumerate() {
+        let mut cfg = EvalConfig::sar_small(pattern);
+        cfg.seeds = seeds;
+        cfg.episodes_per_seed = episodes;
+        let matrix = run_matrix(&cfg);
+        let agg = AggregateMetrics::from_strata(&matrix, 0x0149 ^ idx as u64);
+        eprintln!(
+            "  {}: coverage IQM {:.3}, detection {:.0}%",
+            pattern.name(),
+            agg.coverage_iqm.point,
+            agg.detection_rate * 100.0
+        );
+        rows.push((pattern.name().to_string(), agg));
+    }
+
+    // Rank by descending coverage point estimate.
+    rows.sort_by(|a, b| {
+        b.1.coverage_iqm
+            .point
+            .partial_cmp(&a.1.coverage_iqm.point)
+            .unwrap_or(std::cmp::Ordering::Equal)
+    });
+
+    let md = render_results_md(&rows);
+
+    if let Some(parent) = out.parent() {
+        if let Err(e) = std::fs::create_dir_all(parent) {
+            eprintln!("error: could not create {}: {e}", parent.display());
+            std::process::exit(1);
+        }
+    }
+    if let Err(e) = std::fs::write(&out, &md) {
+        eprintln!("error: could not write {}: {e}", out.display());
+        std::process::exit(1);
+    }
+    eprintln!("Wrote {} ({} bytes).", out.display(), md.len());
+}