diff --git a/docs/research/sota-2026-05-22/R6_2_5-multi-subject-union.md b/docs/research/sota-2026-05-22/R6_2_5-multi-subject-union.md
new file mode 100644
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+# R6.2.5 — Multi-subject occupancy union: N=5 hits 100% for 4 occupants
+
+**Status:** clean positive result · **2026-05-22**
+
+## Premise
+
+R6.2 / R6.2.3 picked one chest position per zone. Real households have 2-4 occupants who can be in different positions simultaneously. R6.2.5 extends to **union of chest envelopes** across all expected occupant positions. The practical question: does coverage degrade gracefully as occupant count grows?
+
+## Result: graceful saturation at N=5
+
+| Scenario | # zones | Total area | Coverage @ N=5 |
+|---|---:|---:|---:|
+| 1 occupant (chair) | 1 | 0.16 m² | **100%** |
+| 2 occupants (chair + bed) | 2 | 0.40 m² | **100%** |
+| 3 occupants (chair + bed + desk) | 3 | 0.48 m² | **100%** |
+| 4 occupants (+ 2nd chair) | 4 | 0.64 m² | **100%** |
+
+**N=5 hits 100% coverage for all configurations up to 4 occupants.** The chest-centric small-zone approach (R6.2.3) generalises trivially to multi-subject.
+
+## 4-occupant saturation curve
+
+| N | Coverage | Marginal |
+|---:|---:|---:|
+| 2 | 14.5% | +14.5 pp |
+| 3 | 72.9% | +58.4 pp |
+| **4** | **99.0%** | **+26.1 pp** |
+| 5 | 100% | +1.0 pp |
+| 6 | 100% | +0 pp |
+| 7 | 100% | +0 pp |
+
+**Knee returns to N=4** — even for 4 occupants, 4 anchors get us to 99%. This is the **2D chest-centric multi-subject** regime, which is the most demanding 2D configuration tested in the R6 family — and it still hits the knee at N=4.
+
+## Cross-eval: single-subject placement is bad for multi-subject
+
+| Placement | Coverage on 4-zone target |
+|---|---:|
+| Single-subject-optimised | 70.6% |
+| Multi-subject-optimised | **100%** |
+| **Gain from multi-subject optimisation** | **+29.4 pp** |
+
+The CLI must accept multiple `--target` arguments and optimise for their **union** — not pick a representative zone and hope.
+
+## Updated CLI recommendation
+
+```bash
+wifi-densepose plan-antennas \
+    --room 5 5 \
+    --target chair_chest 3.7 3.7 0.4 0.4 \
+    --target bed_chest   2.2 0.8 0.6 0.4 \
+    --target desk_chest  0.5 2.7 0.4 0.2 \
+    --target chair2_chest 1.0 4.2 0.4 0.4 \
+    --freq-ghz 2.4
+```
+
+Output: N=5 anchors hitting 100% coverage of the union.
+
+## R6 family summary (8 ticks + this)
+
+| Tick | Configuration | Headline number |
+|---|---|---:|
+| R6.2 | 2D body, single-subject | 51% N=5 |
+| R6.2.1 | 3D body, single-subject | 26% N=2 (mixed-height) |
+| R6.2.2 | 2D body, N-anchor | 97% N=5 |
+| R6.2.2.1 | 3D body, N-anchor | 49% N=5 |
+| R6.2.3 | 2D chest, single-subject | 82% N=5 |
+| R6.2.4 | 3D chest, N-anchor | 77% N=5 / 82% N=6 |
+| **R6.2.5 (this)** | **2D chest, multi-subject (1-4)** | **100% N=5** |
+
+The R6 family's headline finding: **2D chest-centric + multi-subject + N=5 = 100% coverage**. This is the placement recipe to ship.
+
+## Composes with prior threads
+
+- **R6.2 / R6.2.3**: directly extends — single-subject → multi-subject union
+- **R6.2.2 / R6.2.4**: same saturation behaviour at the multi-subject level
+- **R14 (empathic appliances)**: V1 lighting / V2 HVAC / V3 attention in households of 2-4 occupants → use multi-subject placement
+- **R3 / ADR-024**: per-subject identity (AETHER) + multi-subject placement = full empathic-appliance stack
+- **ADR-105 / ADR-106 / ADR-107**: federation operates on the same model across occupant counts; placement is orthogonal
+- **R12 PABS**: works per-subject within the union; multi-subject coverage = multi-subject intrusion detection
+
+## Why N=4 knee returns for multi-subject
+
+Each chest zone is small (40×40 cm) and fits inside a single Fresnel ellipsoid (which is ~40 cm wide at midpoint of a 5 m link). With N=4 anchors, we get 6 pairwise links — enough Fresnel ellipsoids to cover 4 disjoint 40×40 cm zones without much waste. Beyond N=4 the marginal gain drops to <1 pp.
+
+This is *more saturated* than the single-subject R6.2 setup (which used 3 m² bed footprint and couldn't be covered fully even at N=8 with body-centric zones). **Chest-centric multi-subject is the sweet spot for the Fresnel envelope geometry.**
+
+## Honest scope
+
+- **2D only** — multi-subject 3D not benchmarked (extension is mechanical; expect N=6 to retain the chest-centric N=5 advantage).
+- **Static positions** — real occupants move; the union should be conservative (larger than any instantaneous configuration).
+- **Single 5×5 m geometry** — larger or oddly-shaped rooms need separate benchmarks.
+- **Greedy + 4 restarts** — global optimum may be 1-2 pp higher.
+- **4 occupants** — beyond 4-5 the coverage may degrade. Extreme density (e.g. classroom with 20 people) is a different regime.
+
+## What this DOES enable
+
+1. **A clean cap on the placement complexity story**: 4-occupant households are fully sensable at N=5 with multi-subject-aware placement.
+2. **A required CLI feature**: support multiple `--target` arguments.
+3. **An updated installer recipe**: for households of 1-4, the same N=5 chest-centric placement works.
+4. **R6 family closes with a positive result** that ships directly.
+
+## What this DOES NOT enable
+
+- Beyond 4-5 occupants — separate regime, not tested.
+- Time-varying occupancy (people moving between zones) — would benefit from pose-trajectory data (out of scope).
+- 3D multi-subject — mechanical extension, not done here.
+
+## Final R6.2 CLI surface
+
+After this tick, the productisation of R6.2 should support:
+
+```
+wifi-densepose plan-antennas
+    --room W H [Z]                       # 2D or 3D
+    --target NAME X Y W H [DX DY DZ]    # repeatable
+    --target-mode {body, chest}          # R6.2.3
+    --freq-ghz F                         # 2.4, 5.0, 6.0
+    --n-anchors N                        # auto-saturation if omitted
+    --restarts K                         # 4 default
+```
+
+This covers the R6.2 / R6.2.1 / R6.2.2 / R6.2.2.1 / R6.2.3 / R6.2.4 / R6.2.5 use cases in a single CLI tool. ~50 LOC over the original R6.2.
+
+## Connection back
+
+- **R6 / R6.1**: physical foundation
+- **R6.2 / R6.2.3**: single-subject body / chest
+- **R6.2.1 / R6.2.2 / R6.2.2.1 / R6.2.4**: 3D / N-anchor / composition
+- **R6.2.5 (this)**: multi-subject completes the matrix
+- **R14**: empathic-appliance deployment recipe is now: N=5 + chest-centric + multi-subject-union targets, with mixed-height anchors for full-body coverage when needed
diff --git a/docs/research/sota-2026-05-22/ticks/tick-27.md b/docs/research/sota-2026-05-22/ticks/tick-27.md
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+# Tick 27 — 2026-05-22 09:32 UTC
+
+**Thread:** R6.2.5 (multi-subject occupancy union)
+**Verdict:** Clean positive — **N=5 hits 100% coverage** for households of 1-4 occupants with chest-centric zones. N=4 knee returns. R6 family completes with this tick.
+
+## What shipped
+
+- `examples/research-sota/r6_2_5_multi_subject.py`
+- `examples/research-sota/r6_2_5_multi_subject_results.json`
+- `docs/research/sota-2026-05-22/R6_2_5-multi-subject-union.md`
+
+## Headline
+
+| Scenario | # zones | Coverage @ N=5 |
+|---|---:|---:|
+| 1 occupant | 1 | **100%** |
+| 2 occupants | 2 | **100%** |
+| 3 occupants | 3 | **100%** |
+| 4 occupants | 4 | **100%** |
+
+4-occupant saturation curve:
+
+| N | Coverage |
+|---:|---:|
+| 2 | 14.5% |
+| 3 | 72.9% |
+| **4** | **99.0%** ← knee |
+| 5 | 100% |
+
+**Knee at N=4** even for 4 occupants. The chest-centric small-zone approach generalises trivially.
+
+## Cross-eval: multi-subject optimisation matters
+
+| Placement | Coverage on 4 zones |
+|---|---:|
+| Single-subject-optimised | 70.6% |
+| **Multi-subject-optimised** | **100%** |
+| **Gain** | **+29.4 pp** |
+
+CLI must accept multiple `--target` args and compute union.
+
+## R6 family complete (9 ticks)
+
+| Tick | Config | Result |
+|---|---|---:|
+| R6.2 | 2D body, single | 51% N=5 |
+| R6.2.1 | 3D body, single | 26% N=2 |
+| R6.2.2 | 2D body, N-anchor | 97% N=5 |
+| R6.2.2.1 | 3D body, N-anchor | 49% N=5 |
+| R6.2.3 | 2D chest, single | 82% N=5 |
+| R6.2.4 | 3D chest, N-anchor | 77/82% N=5/6 |
+| **R6.2.5** | **2D chest, multi-subject** | **100% N=5** |
+
+**R6 family's ship recipe**: 2D chest-centric + multi-subject + N=5 = 100% coverage.
+
+## Why N=4 knee returns for multi-subject
+
+Each chest zone is 40×40 cm and fits inside one Fresnel ellipsoid (~40 cm wide at midpoint of 5 m link). N=4 anchors → 6 pairwise links → enough to cover 4 disjoint chest zones without much waste. Beyond N=4 the marginal gain drops to <1 pp.
+
+**Chest-centric multi-subject is the sweet spot for the Fresnel envelope geometry.**
+
+## Final R6.2 CLI surface (productisation spec)
+
+```
+wifi-densepose plan-antennas
+    --room W H [Z]                       # 2D or 3D
+    --target NAME X Y W H [DX DY DZ]    # repeatable
+    --target-mode {body, chest}          # R6.2.3
+    --freq-ghz F                         # 2.4, 5.0, 6.0
+    --n-anchors N                        # auto-saturation if omitted
+    --restarts K                         # 4 default
+```
+
+~50 LOC over the original R6.2.
+
+## Composes with prior threads
+
+- R6.2 / R6.2.3: direct extension (single → multi)
+- R6.2.2 / R6.2.4: same saturation behaviour
+- R14: V1/V2/V3 in households of 2-4 use this recipe
+- R3 / ADR-024: per-subject identity + multi-subject placement = full empathic-appliance stack
+- ADR-105/106/107: federation orthogonal to placement
+- R12 PABS: multi-subject coverage = multi-subject intrusion detection
+
+## Honest scope
+
+- 2D only (3D multi-subject is mechanical extension)
+- Static positions (real movement = conservative union)
+- Single 5×5 m geometry
+- Greedy + 4 restarts
+- 4 occupants; beyond may degrade
+
+## Coordination
+
+`ticks/tick-27.md`. No PROGRESS.md edit. Branch `research/sota-r6.2.5-multi-subject`.
+
+## Remaining loop work
+
+- R12.1: pose-PABS closed loop (needs Rust integration, out of scope for synthetic ticks)
+- ADR-108: Kyber substitution (quantum-resistant)
+- Loop retrospective / 00-summary.md (still ~2.5h until cron stop)
+
+~2.5h to cron stop. **27 ticks landed.** R6 family + R3 arc both substantively complete.
diff --git a/examples/research-sota/r6_2_5_multi_subject.py b/examples/research-sota/r6_2_5_multi_subject.py
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--- /dev/null
+++ b/examples/research-sota/r6_2_5_multi_subject.py
@@ -0,0 +1,197 @@
+#!/usr/bin/env python3
+"""R6.2.5 — Multi-subject occupancy union.
+
+See docs/research/sota-2026-05-22/R6_2_5-multi-subject-union.md.
+
+R6.2 / R6.2.3 picked one chest position per zone. Real households
+have 2-4 occupants who can be in different positions simultaneously
+(spouse in bed + child at desk + visitor on chair). R6.2.5 extends to
+**union of chest envelopes** across all expected occupant positions.
+
+Practical question: does the optimal placement degrade gracefully
+when target zones multiply? Does N=5 still hit a useful coverage?
+
+Pure NumPy.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import numpy as np
+
+C = 2.998e8
+
+
+def wavelength_m(freq_ghz: float) -> float:
+    return C / (freq_ghz * 1e9)
+
+
+def in_first_fresnel(x, y, tx, rx, wavelength):
+    r1 = np.sqrt((x - tx[0])**2 + (y - tx[1])**2)
+    r2 = np.sqrt((x - rx[0])**2 + (y - rx[1])**2)
+    direct = np.linalg.norm(tx - rx)
+    return (r1 + r2) <= (direct + wavelength / 2)
+
+
+def union_coverage(anchors, target_x, target_y, wavelength):
+    if len(anchors) < 2: return 0.0
+    covered = np.zeros(len(target_x), dtype=bool)
+    for i in range(len(anchors)):
+        for j in range(i+1, len(anchors)):
+            covered |= in_first_fresnel(target_x, target_y,
+                                       anchors[i], anchors[j], wavelength)
+    return float(covered.mean())
+
+
+def rasterise_zones(zones, resolution=0.05):
+    xs, ys = [], []
+    for name, x0, y0, w, h in zones:
+        zx = np.arange(x0, x0 + w, resolution)
+        zy = np.arange(y0, y0 + h, resolution)
+        gx, gy = np.meshgrid(zx, zy)
+        xs.append(gx.ravel())
+        ys.append(gy.ravel())
+    return np.concatenate(xs), np.concatenate(ys)
+
+
+def candidates(room_w, room_h, step):
+    cands = []
+    for x in np.arange(0, room_w + 0.001, step):
+        cands.append(np.array([x, 0.0]))
+        cands.append(np.array([x, room_h]))
+    for y in np.arange(step, room_h, step):
+        cands.append(np.array([0.0, y]))
+        cands.append(np.array([room_w, y]))
+    return cands
+
+
+def greedy_search(cands, target_x, target_y, lam, n_anchors, restarts=4, seed=0):
+    rng = np.random.default_rng(seed)
+    best = {"score": -1.0, "anchors": []}
+    for r in range(restarts):
+        idx0, idx1 = rng.choice(len(cands), size=2, replace=False)
+        chosen = [cands[idx0], cands[idx1]]
+        while len(chosen) < n_anchors:
+            best_marg = -1
+            best_idx = None
+            for k, c in enumerate(cands):
+                if any(np.allclose(c, a) for a in chosen): continue
+                s = union_coverage(chosen + [c], target_x, target_y, lam)
+                if s > best_marg:
+                    best_marg = s
+                    best_idx = k
+            if best_idx is None: break
+            chosen.append(cands[best_idx])
+        score = union_coverage(chosen, target_x, target_y, lam)
+        if score > best["score"]:
+            best = {"score": score, "anchors": [a.tolist() for a in chosen]}
+    return best
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--out", default="examples/research-sota/r6_2_5_multi_subject_results.json")
+    args = parser.parse_args()
+
+    room_w, room_h = 5.0, 5.0
+    freq = 2.4
+    lam = wavelength_m(freq)
+    step = 0.25
+    cands = candidates(room_w, room_h, step)
+
+    # Scenarios with increasing occupant count
+    # Each "chest zone" is a 40x40 cm patch
+    scenarios = {
+        "1 occupant (chair)": [
+            ("chair_chest", 3.7, 3.7, 0.4, 0.4),
+        ],
+        "2 occupants (chair + bed)": [
+            ("chair_chest", 3.7, 3.7, 0.4, 0.4),
+            ("bed_chest",   2.2, 0.8, 0.6, 0.4),
+        ],
+        "3 occupants (chair + bed + desk)": [
+            ("chair_chest", 3.7, 3.7, 0.4, 0.4),
+            ("bed_chest",   2.2, 0.8, 0.6, 0.4),
+            ("desk_chest",  0.5, 2.7, 0.4, 0.2),
+        ],
+        "4 occupants (+ 2nd chair)": [
+            ("chair_chest",  3.7, 3.7, 0.4, 0.4),
+            ("bed_chest",    2.2, 0.8, 0.6, 0.4),
+            ("desk_chest",   0.5, 2.7, 0.4, 0.2),
+            ("chair2_chest", 1.0, 4.2, 0.4, 0.4),
+        ],
+    }
+
+    print(f"Room {room_w}x{room_h} m, freq {freq} GHz, chest-centric zones")
+    print()
+
+    # For each scenario, find optimum at N=5
+    results = []
+    for name, zones in scenarios.items():
+        tx, ty = rasterise_zones(zones)
+        result = greedy_search(cands, tx, ty, lam, n_anchors=5)
+        # Total zone area
+        zone_area = sum(w * h for _, _, _, w, h in zones)
+        results.append({
+            "scenario": name,
+            "n_zones": len(zones),
+            "total_zone_area_m2": zone_area,
+            "coverage_n5": result["score"],
+            "best_anchors": result["anchors"],
+        })
+
+    print(f"{'Scenario':<40} {'#zones':>6} {'Area':>7} {'Cov@N=5':>9}")
+    print("-" * 75)
+    for r in results:
+        print(f"{r['scenario']:<40} {r['n_zones']:>6} {r['total_zone_area_m2']:>5.2f} m2 {r['coverage_n5']*100:>7.1f}%")
+    print()
+
+    # Stress test: scale N for the 4-occupant scenario
+    print(f"=== 4-occupant scenario, scaling N from 2..7 ===")
+    zones4 = scenarios["4 occupants (+ 2nd chair)"]
+    tx, ty = rasterise_zones(zones4)
+    print(f"{'N':>3}  {'Coverage':>9}  {'Marginal':>9}")
+    prev = 0.0
+    scale_curve = []
+    for n in range(2, 8):
+        result = greedy_search(cands, tx, ty, lam, n_anchors=n)
+        marg = (result["score"] - prev) * 100
+        print(f"{n:>3}  {result['score']*100:>7.1f}%  {marg:>+7.1f} pp")
+        scale_curve.append({"n_anchors": n, "coverage": result["score"]})
+        prev = result["score"]
+    print()
+
+    # Cross-eval: how does a single-subject-optimised placement perform on 4 subjects?
+    single_zone = [("chair_chest", 3.7, 3.7, 0.4, 0.4)]
+    tx1, ty1 = rasterise_zones(single_zone)
+    single_opt = greedy_search(cands, tx1, ty1, lam, n_anchors=5)
+    tx4, ty4 = rasterise_zones(zones4)
+    cov_single_on_multi = union_coverage(
+        [np.array(a) for a in single_opt["anchors"]], tx4, ty4, lam
+    )
+    print(f"=== Cross-eval ===")
+    print(f"  Single-subject placement on 4-subject zones: {cov_single_on_multi*100:.1f}%")
+    print(f"  4-subject-optimised placement on 4 zones:    {results[-1]['coverage_n5']*100:.1f}%")
+    print(f"  Gain from multi-subject optimisation:        {(results[-1]['coverage_n5'] - cov_single_on_multi)*100:+.1f} pp")
+    print()
+
+    out = {
+        "room": {"width_m": room_w, "height_m": room_h},
+        "freq_ghz": freq,
+        "scenarios_n5": results,
+        "saturation_4subj": scale_curve,
+        "cross_eval": {
+            "single_opt_on_multi": cov_single_on_multi,
+            "multi_opt_on_multi": results[-1]["coverage_n5"],
+            "gain_pp": (results[-1]["coverage_n5"] - cov_single_on_multi) * 100,
+        },
+    }
+    Path(args.out).parent.mkdir(parents=True, exist_ok=True)
+    Path(args.out).write_text(json.dumps(out, indent=2))
+    print(f"Wrote {args.out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/research-sota/r6_2_5_multi_subject_results.json b/examples/research-sota/r6_2_5_multi_subject_results.json
new file mode 100644
index 00000000..e5b71ad3
--- /dev/null
+++ b/examples/research-sota/r6_2_5_multi_subject_results.json
@@ -0,0 +1,152 @@
+{
+  "room": {
+    "width_m": 5.0,
+    "height_m": 5.0
+  },
+  "freq_ghz": 2.4,
+  "scenarios_n5": [
+    {
+      "scenario": "1 occupant (chair)",
+      "n_zones": 1,
+      "total_zone_area_m2": 0.16000000000000003,
+      "coverage_n5": 1.0,
+      "best_anchors": [
+        [
+          5.0,
+          3.25
+        ],
+        [
+          0.0,
+          1.25
+        ],
+        [
+          2.0,
+          5.0
+        ],
+        [
+          0.0,
+          0.0
+        ],
+        [
+          0.0,
+          5.0
+        ]
+      ]
+    },
+    {
+      "scenario": "2 occupants (chair + bed)",
+      "n_zones": 2,
+      "total_zone_area_m2": 0.4,
+      "coverage_n5": 1.0,
+      "best_anchors": [
+        [
+          5.0,
+          3.25
+        ],
+        [
+          0.0,
+          1.25
+        ],
+        [
+          5.0,
+          0.5
+        ],
+        [
+          2.0,
+          5.0
+        ],
+        [
+          0.0,
+          0.0
+        ]
+      ]
+    },
+    {
+      "scenario": "3 occupants (chair + bed + desk)",
+      "n_zones": 3,
+      "total_zone_area_m2": 0.48000000000000004,
+      "coverage_n5": 1.0,
+      "best_anchors": [
+        [
+          5.0,
+          3.25
+        ],
+        [
+          0.0,
+          1.25
+        ],
+        [
+          2.0,
+          5.0
+        ],
+        [
+          5.0,
+          0.5
+        ],
+        [
+          0.0,
+          0.0
+        ]
+      ]
+    },
+    {
+      "scenario": "4 occupants (+ 2nd chair)",
+      "n_zones": 4,
+      "total_zone_area_m2": 0.6400000000000001,
+      "coverage_n5": 1.0,
+      "best_anchors": [
+        [
+          3.0,
+          0.0
+        ],
+        [
+          2.5,
+          5.0
+        ],
+        [
+          0.0,
+          3.75
+        ],
+        [
+          4.25,
+          5.0
+        ],
+        [
+          0.75,
+          5.0
+        ]
+      ]
+    }
+  ],
+  "saturation_4subj": [
+    {
+      "n_anchors": 2,
+      "coverage": 0.14516129032258066
+    },
+    {
+      "n_anchors": 3,
+      "coverage": 0.7290322580645161
+    },
+    {
+      "n_anchors": 4,
+      "coverage": 0.9903225806451613
+    },
+    {
+      "n_anchors": 5,
+      "coverage": 1.0
+    },
+    {
+      "n_anchors": 6,
+      "coverage": 1.0
+    },
+    {
+      "n_anchors": 7,
+      "coverage": 1.0
+    }
+  ],
+  "cross_eval": {
+    "single_opt_on_multi": 0.7064516129032258,
+    "multi_opt_on_multi": 1.0,
+    "gain_pp": 29.354838709677423
+  }
+}
\ No newline at end of file