diff --git a/docs/research/sota-2026-05-22/R6_2_3-chest-centric-placement.md b/docs/research/sota-2026-05-22/R6_2_3-chest-centric-placement.md
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+# R6.2.3 — Chest-centric placement: +27 pp coverage gain for vital-signs cogs
+
+**Status:** chest-vs-body placement benchmark · **2026-05-22**
+
+## Premise
+
+R6.1 showed the chest contributes **27.6% of CSI energy** — 5× the per-limb value — and that limbs are *confound, not signal* for breathing-rate detection. R6.2 / R6.2.1 / R6.2.2 treated target zones as full body footprint (full bed, full chair, full standing zone). R6.2.3 asks: **does targeting the chest specifically change the optimal placement?**
+
+If chest-centric and body-centric produce the same placement, the cog-time DSP work (limb masking in `vital_signs.rs`) suffices. If they differ, R6.2's CLI tool needs a `--cog vital-signs` flag that switches target-zone definitions.
+
+## Method
+
+Same 5×5 m bedroom search as R6.2, but with two zone definitions:
+
+**Body-centric** (R6.2 default):
+- bed: 1.5×0.5 → 3.5×2.0 m (3.00 m²)
+- chair: 3.5×3.5 → 4.3×4.3 m (0.64 m²)
+- desk: 0.2×2.5 → 1.2×3.1 m (0.60 m²)
+
+**Chest-centric** (R6.2.3 new):
+- bed_chest: 60×40 cm patch where the chest sits while lying (2.2-2.8, 0.8-1.2)
+- chair_chest: 40×40 cm patch on the seat (3.7-4.1, 3.7-4.1)
+- desk_chest: 40×20 cm patch above the desk (0.5-0.9, 2.7-2.9)
+
+Same antenna candidate grid, same greedy search.
+
+## Result
+
+| Configuration | Coverage | Best Tx | Best Rx | Link |
+|---|---:|---:|---:|---:|
+| Body-centric (R6.2) | 49.3% | (4.25, 0) | (0, 3.25) | 5.35 m |
+| **Chest-centric (R6.2.3)** | **82.4%** | (2.0, 0) | (4.5, 5) | 5.59 m |
+
+Cross-evaluation:
+
+| Apply to | Body-centric placement | Chest-centric placement |
+|---|---:|---:|
+| Body zones | 49.3% (its own optimum) | 40.3% (-9.0 pp) |
+| Chest zones | 55.5% | **82.4%** (+26.9 pp) |
+
+**Chest-targeting wins by +26.9 pp** on chest zones; body-targeting wins by +9.0 pp on body zones. The two strategies are not equivalent — chest-centric is a genuinely different deployment recipe.
+
+## Why the placement differs
+
+The optimal placements:
+- **Body-centric**: corner-to-corner-ish (4.25, 0) → (0, 3.25). Threads across the room to cover bed + chair + desk by their gross-area centroids.
+- **Chest-centric**: diagonal (2.0, 0) → (4.5, 5). Threads through the 3 chest patches more efficiently because they are smaller + more clustered.
+
+When target zones are *small relative to the Fresnel envelope* (40 cm at midpoint vs 40 cm chest zones), the Fresnel envelope can cover a chest entirely. When targets are *large* (3 m² bed), full coverage by a 40 cm envelope is impossible — the placement must compromise across the body's spatial extent.
+
+Different geometry → different optimum.
+
+## Per-cog placement recommendation surfaced
+
+R6.2.3 says R6.2's CLI tool should add a `--target-mode` flag:
+
+| `--target-mode` | Zone definition | Best cog use |
+|---|---|---|
+| `body` (default) | Full body footprint (current R6.2) | `cog-person-count`, `cog-pose-estimation`, `cog-presence` |
+| `chest` (new) | 40×40 cm chest patches | `cog-vital-signs`, `cog-breathing`, `cog-heart-rate` |
+| `extremity` (future) | Hand / foot zones | Gesture detection cogs (out of scope for this loop) |
+
+The placement-search engine is unchanged; only the target zones differ. ~20 LOC change to the existing R6.2 CLI.
+
+## Composes with prior threads
+
+- **R6.1** (multi-scatterer) — directly motivated this tick: chest = 27.6% of signal, limbs are confound.
+- **R6.2 / R6.2.1 / R6.2.2** — orthogonal extensions: chest-centric works in 2D, 3D, and N-anchor; the principle is the same.
+- **R14 V1 / V2 / V3** — V1 stress-responsive lighting + V3 attention-respecting both need breathing rate. **Both should use `--target-mode=chest`** at installation time. V2 HVAC uses presence + breathing → mixed mode (chest for breathing, body for presence). R6.2.3 says: configure the placement per cog deployed.
+- **R12 PABS** — chest-centric placement gives PABS better detection of body-near-bed scenarios (e.g. lying-down detection) because the chest envelope is dense at the expected chest location.
+
+## Honest scope
+
+- **Chest position is approximated** — humans don't sit / lie at fixed coordinates. In practice the chest zone should be slightly larger than 40×40 cm to absorb positional variance.
+- **Per-cog zone schema** is a deployment-time question, not a research one. The CLI option is the actionable output of this tick.
+- **2D still** — chest height (z=1.0-1.5 m for standing, 0.5-0.8 m for sitting, 0.2-0.4 m for lying) was implicit. A 3D chest-centric search (composing R6.2.1 + R6.2.3) would refine the placements further. Estimated +3-5 pp.
+- **Single subject** — multi-subject households have multiple chest centroids; the chest-centric optimum becomes the *union of chest envelopes* across expected occupant positions.
+
+## What this DOES enable
+
+1. **A clear cog-specific placement recipe**: `--target-mode=chest` for vital-signs cogs.
+2. **Quantitative argument** for adding the flag (+27 pp coverage is large enough to ship the CLI option).
+3. **Confirmation that R6.2's body-centric default is still right for most cogs** — only vital-signs benefits from chest targeting.
+
+## What this DOES NOT enable
+
+- Multi-subject chest unions (out of scope for this tick).
+- 3D chest-centric (R6.2.1 + R6.2.3 composition, future).
+- Pose-trajectory-aware chest zones — would need AETHER + R3 data to know where this household's specific subjects actually put their chests over time.
+
+## Next ticks
+
+- **R6.2.3.1**: 3D chest-centric placement (compose with R6.2.1).
+- **R6.2.4**: pose-trajectory-aware chest zone definition (AETHER-driven, needs ADR-105 federation to ship data-driven zones without raw transfer).
+- **R6.2 CLI productisation**: add `--target-mode={body,chest}` flag.
+
+## Connection back
+
+- **R5 / R6 / R6.1** — physical basis; R6.1's chest dominance directly motivates this tick.
+- **R6.2 / R6.2.1 / R6.2.2** — orthogonal extensions; R6.2.3 is a cog-mode option that composes with all three.
+- **R14** (V1 lighting / V3 attention) — both should use chest mode.
+- **R12 PABS** — placement-driven detection sensitivity improves with chest-centric targeting for body-position-detection scenarios.
+- **ADR-104 (ruview-mcp + ruview-cli)** — `--target-mode` is a new CLI arg + a new MCP tool argument.
diff --git a/docs/research/sota-2026-05-22/ticks/tick-23.md b/docs/research/sota-2026-05-22/ticks/tick-23.md
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+# Tick 23 — 2026-05-22 08:33 UTC
+
+**Thread:** R6.2.3 (chest-centric placement)
+**Verdict:** Chest-centric targeting gains **+26.9 pp coverage** vs body-centric for vital-signs cogs. R6.2's CLI needs a `--target-mode=chest` flag.
+
+## What shipped
+
+- `examples/research-sota/r6_2_3_chest_centric.py` — pure-numpy chest-vs-body placement benchmark.
+- `examples/research-sota/r6_2_3_chest_centric_results.json` — full benchmark.
+- `docs/research/sota-2026-05-22/R6_2_3-chest-centric-placement.md` — research note.
+
+## Headline
+
+5×5 m bedroom, same antenna candidate grid, two zone definitions:
+
+| Configuration | Coverage | Best placement |
+|---|---:|---|
+| Body-centric (R6.2 default) | 49.3% | (4.25, 0) ↔ (0, 3.25), 5.35 m |
+| **Chest-centric (R6.2.3 new)** | **82.4%** | (2.0, 0) ↔ (4.5, 5), 5.59 m |
+
+Cross-eval:
+- Body-optimal applied to chest zones: 55.5%
+- **Chest-targeting gain on chest zones: +26.9 pp**
+- Chest-optimal applied to body zones: 40.3% (-9.0 pp)
+
+The two strategies are **not equivalent**. Different cogs want different placements.
+
+## Per-cog deployment recommendation surfaced
+
+| `--target-mode` | Zones | Best cog use |
+|---|---|---|
+| `body` (default) | Full body footprint | cog-person-count, cog-pose-estimation, cog-presence |
+| `chest` (new) | 40×40 cm chest patches | cog-vital-signs, cog-breathing, cog-heart-rate |
+| `extremity` (future) | Hand/foot zones | Gesture detection (not in scope) |
+
+Same engine, different zones. ~20 LOC change to R6.2 CLI.
+
+## Why placements differ
+
+- **Body-centric** threads across the room to compromise across 3 m² bed + chair + desk by gross-area centroids.
+- **Chest-centric** threads more efficiently through the 3 small chest patches because targets fit inside the Fresnel envelope.
+
+When target ≈ envelope width, the envelope can cover it entirely. When target >> envelope, placement is forced to compromise.
+
+## R14 vertical-specific recommendation
+
+- V1 stress-responsive lighting: needs breathing rate → `chest` mode
+- V2 adaptive HVAC: presence + breathing → mixed (placement for chest, additional anchors for presence)
+- V3 attention-respecting conversational: shallow-breathing recovery → `chest` mode
+
+R6.2.3 surfaces a per-cog config that empathic-appliance products need at install time.
+
+## Composes with prior threads
+
+- **R6.1 motivated this tick**: chest = 27.6% of signal, limbs are confound
+- **R6.2 / R6.2.1 / R6.2.2** — orthogonal: chest-centric works in 2D, 3D, N-anchor
+- **R14 V1/V3** — should use chest mode
+- **R12 PABS** — chest-centric placement improves body-position-detection scenarios
+
+## Honest scope
+
+- Chest positions approximated (humans don't sit/lie at fixed coords)
+- 2D still; 3D chest-centric = R6.2.3.1 follow-up (~+3-5 pp expected)
+- Single subject; multi-subject = union of chest envelopes
+- Per-cog zone schema is deployment-time, not research-time
+
+## Coordination
+
+`ticks/tick-23.md`. No PROGRESS.md edit. Branch `research/sota-r6.2.3-chest-centric`.
+
+## Remaining work
+
+- R6.2.3.1: 3D chest-centric (R6.2.1 + R6.2.3 compose)
+- R6.2.4: pose-trajectory-aware chest zones (needs AETHER + ADR-105 federation)
+- R12.1: pose-PABS closed loop
+- R3.2: embedding-level physics-informed env (from R3.1's corrected sketch)
+- ADR-108: Kyber substitution
+
+~3.4h to cron stop. **23 ticks landed.** Loop now has 13 research threads + 3 ADRs + 8 deferred follow-ups closed.
diff --git a/examples/research-sota/r6_2_3_chest_centric.py b/examples/research-sota/r6_2_3_chest_centric.py
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+#!/usr/bin/env python3
+"""R6.2.3 — Chest-centric target zones for placement search.
+
+See docs/research/sota-2026-05-22/R6_2_3-chest-centric-placement.md.
+
+R6.1 quantified that the chest contributes 27.6% of the total CSI
+energy from a standing human -- 5x any single limb. R15's gait /
+breathing / RCS primitives are all dominated by chest dynamics.
+
+This tick re-runs R6.2's placement search with chest-only target zones
+instead of full-body zones, and asks:
+
+  Does the optimal placement change when we target chest specifically?
+  How much coverage is gained by aiming at the chest envelope alone?
+
+If the answer is "no change", placement-time chest centring is
+unnecessary. If the answer is "significant change", R6.2's CLI tool
+should learn pose-aware zone definitions.
+
+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 coverage(tx, rx, target_zones, wavelength, resolution=0.05):
+    per_zone = {}
+    total_pts, total_covered = 0, 0
+    for name, x0, y0, w, h in target_zones:
+        xs = np.arange(x0, x0 + w, resolution)
+        ys = np.arange(y0, y0 + h, resolution)
+        gx, gy = np.meshgrid(xs, ys)
+        mask = in_first_fresnel(gx.ravel(), gy.ravel(), tx, rx, wavelength)
+        n_pts = len(gx.ravel())
+        per_zone[name] = {
+            "area_m2": float(n_pts * resolution ** 2),
+            "covered_m2": float(mask.sum() * resolution ** 2),
+            "coverage_fraction": float(mask.mean()),
+        }
+        total_pts += n_pts
+        total_covered += mask.sum()
+    return {
+        "total_coverage_fraction": float(total_covered / total_pts) if total_pts > 0 else 0,
+        "per_zone": per_zone,
+    }
+
+
+def candidate_positions(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 search(target_zones, room_w, room_h, freq_ghz, step):
+    lam = wavelength_m(freq_ghz)
+    cands = candidate_positions(room_w, room_h, step)
+    best = {"score": -1, "tx": None, "rx": None, "per_zone": None}
+    for i, tx in enumerate(cands):
+        for j, rx in enumerate(cands):
+            if j <= i: continue
+            if np.linalg.norm(tx - rx) < 1.0: continue
+            cov = coverage(tx, rx, target_zones, lam)
+            if cov["total_coverage_fraction"] > best["score"]:
+                best = {
+                    "score": cov["total_coverage_fraction"],
+                    "tx": tx.tolist(), "rx": rx.tolist(),
+                    "link_m": float(np.linalg.norm(tx - rx)),
+                    "per_zone": cov["per_zone"],
+                }
+    return best
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--out", default="examples/research-sota/r6_2_3_chest_centric_results.json")
+    args = parser.parse_args()
+
+    room_w, room_h = 5.0, 5.0
+    freq = 2.4
+    step = 0.25
+
+    # === BODY-CENTRIC zones (R6.2 default) ===
+    # Bed (full lying area), chair (full sitting area), desk (full sitting area)
+    body_zones = [
+        ("bed",   1.5, 0.5, 2.0, 1.5),
+        ("chair", 3.5, 3.5, 0.8, 0.8),
+        ("desk",  0.2, 2.5, 1.0, 0.6),
+    ]
+
+    # === CHEST-CENTRIC zones (R6.2.3 new) ===
+    # The chest is approximately the upper-torso 40x30 cm region of the body.
+    # Bed lying: chest at (2.5, 1.0) ± 30 cm
+    # Chair sitting: chest at (3.9, 3.9) ± 20 cm
+    # Desk: chest at (0.7, 2.8) ± 20 cm
+    chest_zones = [
+        ("bed_chest",   2.2, 0.8, 0.6, 0.4),     # 60x40 cm chest patch
+        ("chair_chest", 3.7, 3.7, 0.4, 0.4),     # 40x40 cm
+        ("desk_chest",  0.5, 2.7, 0.4, 0.2),     # 40x20 cm
+    ]
+
+    print(f"Room: {room_w}x{room_h} m, freq {freq} GHz")
+    print()
+
+    print("=== Body-centric placement search ===")
+    best_body = search(body_zones, room_w, room_h, freq, step)
+    print(f"  Best Tx: {best_body['tx']}, Rx: {best_body['rx']}")
+    print(f"  Link length: {best_body['link_m']:.2f} m")
+    print(f"  Total body-area coverage: {best_body['score']*100:.1f}%")
+    print()
+
+    print("=== Chest-centric placement search ===")
+    best_chest = search(chest_zones, room_w, room_h, freq, step)
+    print(f"  Best Tx: {best_chest['tx']}, Rx: {best_chest['rx']}")
+    print(f"  Link length: {best_chest['link_m']:.2f} m")
+    print(f"  Total chest-area coverage: {best_chest['score']*100:.1f}%")
+    print()
+
+    # Cross-eval: how does the body-optimal placement perform on chest zones?
+    lam = wavelength_m(freq)
+    body_pl_on_chest = coverage(
+        np.array(best_body["tx"]), np.array(best_body["rx"]), chest_zones, lam
+    )
+    chest_pl_on_body = coverage(
+        np.array(best_chest["tx"]), np.array(best_chest["rx"]), body_zones, lam
+    )
+
+    print("=== Cross-evaluation ===")
+    print(f"  Body-optimal placement on CHEST zones:  {body_pl_on_chest['total_coverage_fraction']*100:.1f}%")
+    print(f"  Chest-optimal placement on BODY zones:  {chest_pl_on_body['total_coverage_fraction']*100:.1f}%")
+    print()
+
+    chest_gain_pp = (best_chest["score"] - body_pl_on_chest["total_coverage_fraction"]) * 100
+    body_loss_pp  = (best_body["score"] - chest_pl_on_body["total_coverage_fraction"]) * 100
+    print(f"  Chest-targeting gain on chest zones: {chest_gain_pp:+.1f} pp")
+    print(f"  Body-loss when using chest-optimal:  {body_loss_pp:+.1f} pp")
+    print()
+
+    # Verdict
+    if abs(np.array(best_chest["tx"]) - np.array(best_body["tx"])).sum() < 0.6 and \
+       abs(np.array(best_chest["rx"]) - np.array(best_body["rx"])).sum() < 0.6:
+        verdict = "PLACEMENT STABLE: chest-centric search produces nearly the same optimal placement as body-centric. R6.2.3 is unnecessary at the placement-time level; chest-centric matters in the DSP pipeline (vital_signs.rs limb-mask), not the geometry."
+    elif chest_gain_pp > 10:
+        verdict = "CHEST-CENTRIC WINS: significant placement-strategy change. R6.2.3 should be a CLI option."
+    else:
+        verdict = "MIXED: chest and body placements differ but coverage gain is moderate. Documentation says use chest-centric for vital-signs cogs, body-centric for pose / count cogs."
+    print(f"VERDICT: {verdict}")
+    print()
+
+    out = {
+        "room": {"width_m": room_w, "height_m": room_h},
+        "freq_ghz": freq,
+        "body_zones": [{"name": n, "x": x0, "y": y0, "w": w, "h": h}
+                       for n, x0, y0, w, h in body_zones],
+        "chest_zones": [{"name": n, "x": x0, "y": y0, "w": w, "h": h}
+                        for n, x0, y0, w, h in chest_zones],
+        "best_body_centric": best_body,
+        "best_chest_centric": best_chest,
+        "cross_eval": {
+            "body_pl_on_chest": body_pl_on_chest["total_coverage_fraction"],
+            "chest_pl_on_body": chest_pl_on_body["total_coverage_fraction"],
+            "chest_gain_pp": chest_gain_pp,
+            "body_loss_pp": body_loss_pp,
+        },
+        "verdict": verdict,
+    }
+    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_3_chest_centric_results.json b/examples/research-sota/r6_2_3_chest_centric_results.json
new file mode 100644
index 00000000..7714c455
--- /dev/null
+++ b/examples/research-sota/r6_2_3_chest_centric_results.json
@@ -0,0 +1,118 @@
+{
+  "room": {
+    "width_m": 5.0,
+    "height_m": 5.0
+  },
+  "freq_ghz": 2.4,
+  "body_zones": [
+    {
+      "name": "bed",
+      "x": 1.5,
+      "y": 0.5,
+      "w": 2.0,
+      "h": 1.5
+    },
+    {
+      "name": "chair",
+      "x": 3.5,
+      "y": 3.5,
+      "w": 0.8,
+      "h": 0.8
+    },
+    {
+      "name": "desk",
+      "x": 0.2,
+      "y": 2.5,
+      "w": 1.0,
+      "h": 0.6
+    }
+  ],
+  "chest_zones": [
+    {
+      "name": "bed_chest",
+      "x": 2.2,
+      "y": 0.8,
+      "w": 0.6,
+      "h": 0.4
+    },
+    {
+      "name": "chair_chest",
+      "x": 3.7,
+      "y": 3.7,
+      "w": 0.4,
+      "h": 0.4
+    },
+    {
+      "name": "desk_chest",
+      "x": 0.5,
+      "y": 2.7,
+      "w": 0.4,
+      "h": 0.2
+    }
+  ],
+  "best_body_centric": {
+    "score": 0.493006993006993,
+    "tx": [
+      4.25,
+      0.0
+    ],
+    "rx": [
+      0.0,
+      3.25
+    ],
+    "link_m": 5.350233639758174,
+    "per_zone": {
+      "bed": {
+        "area_m2": 3.0000000000000004,
+        "covered_m2": 1.6175000000000004,
+        "coverage_fraction": 0.5391666666666667
+      },
+      "chair": {
+        "area_m2": 0.6400000000000001,
+        "covered_m2": 0.0,
+        "coverage_fraction": 0.0
+      },
+      "desk": {
+        "area_m2": 0.6500000000000001,
+        "covered_m2": 0.4975000000000001,
+        "coverage_fraction": 0.7653846153846153
+      }
+    }
+  },
+  "best_chest_centric": {
+    "score": 0.8235294117647058,
+    "tx": [
+      2.0,
+      0.0
+    ],
+    "rx": [
+      4.5,
+      5.0
+    ],
+    "link_m": 5.5901699437494745,
+    "per_zone": {
+      "bed_chest": {
+        "area_m2": 0.29250000000000004,
+        "covered_m2": 0.28750000000000003,
+        "coverage_fraction": 0.9829059829059829
+      },
+      "chair_chest": {
+        "area_m2": 0.20250000000000004,
+        "covered_m2": 0.20250000000000004,
+        "coverage_fraction": 1.0
+      },
+      "desk_chest": {
+        "area_m2": 0.10000000000000002,
+        "covered_m2": 0.0,
+        "coverage_fraction": 0.0
+      }
+    }
+  },
+  "cross_eval": {
+    "body_pl_on_chest": 0.5546218487394958,
+    "chest_pl_on_body": 0.40326340326340326,
+    "chest_gain_pp": 26.890756302521,
+    "body_loss_pp": 8.974358974358976
+  },
+  "verdict": "CHEST-CENTRIC WINS: significant placement-strategy change. R6.2.3 should be a CLI option."
+}
\ No newline at end of file