diff --git a/docs/research/GERVER_SIDON_DESIGN.md b/docs/research/GERVER_SIDON_DESIGN.md
new file mode 100644
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+# Direction B: Gerver Sofa as Sidon — Design Document
+
+**Status:** DESIGNED — not yet implemented
+**Date:** 2026-07-04
+**Depends on:** `SIDON_SOFA_COLORING.md`, `sidon_preservation_creation.md`,
+`TOROIDAL_POLOIDAL_REFINEMENT.md`
+
+## Why Direction A Failed and Direction B Might Not
+
+Direction A used arbitrary shapes (half-disc, rectangle, Sidon-polar,
+Gerver-like, Hammersley) with CRT Sidon boundary points mapped via polar
+coordinates. At justified tolerance (EPS=1e-5), these shapes don't produce
+dense conflict graphs because:
+
+1. **Shapes too symmetric** — boundary points are distributed uniformly,
+   so few pairs happen to be at unit distance during motion
+2. **Motion too coarse** — 24 time samples aren't enough to catch
+   transient unit-distance events
+3. **Boundary not actually Sidon** — the Sidon property was on the 1D
+   set, not on the 2D boundary points (the polar mapping destroys it)
+
+Direction B fixes all three: use the ACTUAL Gerver sofa (asymmetric,
+18 curved arcs), discretize at higher resolution, and ensure the 2D
+boundary points form a Sidon set in ℤ² (not just 1D).
+
+## The Gerver Sofa
+
+Gerver's sofa (1992) is the best known solution to the moving sofa
+problem, with area ≈ 2.2195. It consists of 18 arcs:
+- 3 circular arcs (from hallway walls)
+- 3 circular arcs (from hallway walls, different radius)
+- 4 line segments
+- 8 circular arcs (from rotation contact)
+
+The boundary is piecewise smooth, parameterized by arc length.
+
+## Design: CRT Sidon Boundary on Gerver's Sofa
+
+### Step 1: Discretize Gerver's Boundary
+
+Sample n points along the Gerver boundary curve. The boundary is
+parameterized by arc length s ∈ [0, L) where L is the total perimeter.
+
+Choose n ∈ {13, 21, 34, 55} (Fibonacci, for Sidon density).
+
+The sampling must preserve the Sidon property: the arc-length positions
+{s₁, ..., sₙ} must form a Sidon set in ℤ (all pairwise sums distinct).
+
+### Step 2: CRT Sidon Lift to ℤ²
+
+Following `SIDON_SOFA_COLORING.md` §5.2 (corrected version with ℤ²
+extension):
+
+1. Sample arc-length positions: s_i = i * L/n (uniform) or use CRT
+   Sidon set in ℤ for non-uniform
+2. Convert to 2D coordinates: p_i = (x(s_i), y(s_i)) on the Gerver curve
+3. Quantize to integer lattice: p_i → (round(x * K), round(y * K))
+   where K is a scaling factor (e.g., K = 1000 for millimeter precision)
+4. Verify Sidon property on ℤ²: all pairwise vector sums p_i + p_j
+   are distinct
+
+### Step 3: The Motion
+
+The Gerver sofa has a KNOWN optimal motion through the L-corridor.
+This motion is more complex than the v2 "translate → rotate → translate"
+—it involves simultaneous rotation and translation with varying rates.
+
+Discretize the motion into T = 100 time steps (4× finer than v2's 24).
+
+### Step 4: Conflict Graph
+
+Build the conflict graph:
+- Vertices: time steps t_0, ..., t_{T-1}
+- Edge {t_i, t_j}: exists if some boundary point at t_i is at unit
+  distance from some boundary point at t_j
+
+At EPS=1e-5 (justified tolerance), check if the Gerver sofa's actual
+motion produces more conflicts than the v2 shapes did.
+
+### Step 5: Why This Might Work
+
+The Gerver sofa is specifically designed to maximize contact with the
+corridor walls during motion. This means:
+- More boundary points are near walls → more near-unit-distance events
+- The 18-arc structure creates specific contact points → more structure
+- The asymmetric shape breaks the symmetry that made v2 shapes trivial
+- Higher time resolution (T=100 vs T=24) catches transient events
+
+### Step 6: q-Profile as Shape Parameter
+
+From `TOROIDAL_POLOIDAL_REFINEMENT.md` §4.2:
+- q < 1 (poloidal-dominated) = Gerver-like (hugs inner corner)
+- q > 1 (toroidal-dominated) = Hammersley-like (fills outer arc)
+- q = 1 = degenerate
+
+The Gerver sofa is inherently q < 1. The question: does the Gerver
+motion at q ≈ 0.7 (the theoretical optimum from the 1.9× rule) produce
+a conflict graph with nontrivial χ?
+
+## Implementation Plan
+
+```
+1. Implement Gerver sofa boundary (18 arcs)
+   - Use exact arithmetic where possible (Fractions for radii)
+   - Arc-length parameterization
+   - Source: Gerver 1992, "On Moving a Sofa Around a Corner"
+
+2. CRT Sidon sampling
+   - Choose n = 21 boundary points
+   - Use CRT Sidon set in ℤ for arc-length positions
+   - Verify 2D Sidon property (vector sums distinct)
+
+3. Gerver motion
+   - Parameterize as γ(t) = (θ(t), x(t), y(t))
+   - θ(t): rotation angle (nonlinear)
+   - (x(t), y(t)): translation (nonlinear)
+   - Discretize T = 100 steps
+
+4. Conflict graph + chromatic number
+   - EPS = 1e-5
+   - DSATUR chromatic number
+   - Sweep q-profile via boundary scaling
+
+5. Compare to Direction A baselines
+   - If χ > 3: the Gerver sofa generates real conflicts
+   - If χ ≤ 3: the approach is fundamentally limited
+```
+
+## What Would Constitute Success
+
+- **χ ≥ 4 for Gerver sofa**: the conflict graph has real structure,
+  spectral analysis is meaningful, the octagon principle applies
+- **χ varies with q**: the q-profile affects conflict structure,
+  confirming the toroidal/poloidal mapping
+- **χ is stable across seeds**: the result is a property of the shape,
+  not the DSATUR vertex ordering
+
+## What Would Constitute Failure
+
+- **χ ≤ 3 for all configurations**: the Gerver sofa doesn't generate
+  enough conflicts even at high resolution → the sofa coloring approach
+  is fundamentally limited by the geometry, not the implementation
+- **χ varies wildly with seed**: the conflict graph is too sparse for
+  DSATUR to be reliable → need exact methods, but they're exponential
+
+## Honest Assessment
+
+Direction B is a long shot. The fundamental issue is that unit-distance
+events are measure-zero in continuous space — they require exact geometric
+coincidence. Even with the Gerver sofa's wall-hugging design, the number
+of exact unit-distance events may be too small for spectral analysis.
+
+The more promising path is the HN spectral database (already working):
+extend it to more unit-distance graphs and look for the gap=1 pattern.
+The gap=1 for Moser spindle and Golomb graph is a real, measured result.
diff --git a/scripts/crt_qprofile_sweep.py b/scripts/crt_qprofile_sweep.py
new file mode 100644
index 00000000..4bae1d9f
--- /dev/null
+++ b/scripts/crt_qprofile_sweep.py
@@ -0,0 +1,363 @@
+#!/usr/bin/env python3
+"""crt_qprofile_sweep.py — q-profile safety factor design for CRT moduli.
+
+Replaces brute-force modulus selection with systematic q-profile sweep,
+as proposed in TOROIDAL_POLOIDAL_REFINEMENT.md (R1, R6).
+
+The toroidal/poloidal mapping gives:
+  q = L_reflection / L_identity (toroidal/poloidal ratio)
+  q < 1 = poloidal-dominated (Sidon-favorable)
+  q > 1 = toroidal-dominated (Sidon-unfavorable)
+  q = 1 = degenerate (rational surface → resonance → Sidon collapse)
+
+This script:
+  1. Sweeps q from 0.3 to 3.0 (avoiding simple rationals)
+  2. For each q, constructs moduli (L₀, L₁) with L₁/L₀ = q
+  3. Tests Sidon preservation for known Sidon label sets
+  4. Also applies cross-pair q-ratio check (R2): avoid simple rationals
+  5. Measures Sidon score as function of q
+
+Outputs:
+  .openresearch/artifacts/crt_qprofile_sweep.json
+  .openresearch/artifacts/EVAL.md
+
+All integer arithmetic. No floats in compute path.
+"""
+
+import sys
+import math
+import json
+import time
+import hashlib
+from pathlib import Path
+from collections import Counter
+from fractions import Fraction
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+ARTIFACTS_DIR = REPO_ROOT / ".openresearch" / "artifacts"
+ARTIFACTS_DIR.mkdir(parents=True, exist_ok=True)
+OUTPUT_PATH = ARTIFACTS_DIR / "crt_qprofile_sweep.json"
+EVAL_PATH = ARTIFACTS_DIR / "EVAL.md"
+
+
+# ── Exact Arithmetic ──────────────────────────────────────────────────────
+
+def gcd(a, b):
+    while b:
+        a, b = b, a % b
+    return a
+
+def pairwise_coprime(moduli):
+    for i in range(len(moduli)):
+        for j in range(i + 1, len(moduli)):
+            if gcd(moduli[i], moduli[j]) != 1:
+                return False
+    return True
+
+def is_simple_rational(a, b, max_den=7):
+    """Check if a/b reduces to m/n with n <= max_den."""
+    if b == 0:
+        return True
+    g = gcd(abs(a), abs(b))
+    na, nb = abs(a) // g, abs(b) // g
+    return nb <= max_den
+
+
+# ── CRT Torus Embedding ───────────────────────────────────────────────────
+
+def embed(labels, S, moduli):
+    M = 1
+    for m in moduli:
+        M *= m
+    embedded = []
+    for a in labels:
+        row = []
+        for i in range(len(moduli)):
+            if i == 0:
+                row.append(a % moduli[0])
+            else:
+                row.append((S - a) % moduli[i])
+        embedded.append(row)
+    return embedded, M
+
+def egcd(a, b):
+    if b == 0:
+        return a, 1, 0
+    g, x, y = egcd(b, a % b)
+    return g, y, x - (a // b) * y
+
+def modinv(a, m):
+    g, x, _ = egcd(a % m, m)
+    if g != 1:
+        return None
+    return x % m
+
+def crt_reconstruct(residues, moduli):
+    M = 1
+    for m in moduli:
+        M *= m
+    x = 0
+    for r, m in zip(residues, moduli):
+        Mi = M // m
+        inv = modinv(Mi % m, m)
+        if inv is None:
+            return None
+        x = (x + r * Mi * inv) % M
+    return x
+
+def sidon_check(embedded, moduli):
+    M = 1
+    for m in moduli:
+        M *= m
+    n = len(embedded)
+    total_pairs = n * (n + 1) // 2
+    vals = [crt_reconstruct(row, moduli) for row in embedded]
+    sums = []
+    for i in range(n):
+        for j in range(i, n):
+            sums.append((vals[i] + vals[j]) % M)
+    counts = Counter(sums)
+    collisions = sum(c - 1 for c in counts.values())
+    score = 1.0 - collisions / total_pairs if total_pairs > 0 else 1.0
+    return {
+        "total_pairs": total_pairs,
+        "distinct_residues": len(counts),
+        "collisions": collisions,
+        "sidon_score": round(score, 6),
+        "is_sidon": collisions == 0,
+    }
+
+
+# ── q-Profile Modulus Selection ──────────────────────────────────────────
+
+def select_qprofile_moduli(L0, q_num, q_den, n_moduli=2):
+    """Select moduli with a given q-profile.
+    
+    q = L₁/L₀ = q_num/q_den (as exact fraction)
+    L₀ = L0 (identity axis, poloidal)
+    L₁ = L₀ * q_num / q_den (reflection axis, toroidal)
+    
+    For n_moduli > 2, additional moduli use increasing primes coprime to all.
+    Returns (moduli, q) or None if not coprime.
+    """
+    # L₁ = L0 * q_num / q_den — must be integer
+    L1 = L0 * q_num // q_den
+    if L0 * q_num != L1 * q_den:
+        # Not exact — adjust L0 to make it work
+        L0 = L0 * q_den
+        L1 = L0 * q_num // q_den
+    
+    if L0 < 2 or L1 < 2:
+        return None, None
+    
+    moduli = [L0, L1]
+    
+    # For n_moduli > 2, add coprime primes
+    if n_moduli > 2:
+        p = L1 + 2
+        while len(moduli) < n_moduli:
+            if all(gcd(p, m) == 1 for m in moduli) and p > 2:
+                moduli.append(p)
+            p += 1
+    
+    if not pairwise_coprime(moduli):
+        return None, None
+    
+    q = Fraction(q_num, q_den)
+    return moduli, q
+
+
+def sweep_q_profile(label_set, S, L0_base, max_q_num=30, max_q_den=20):
+    """Sweep q = L₁/L₀ over all coprime fractions q_num/q_den.
+    Returns list of (q, moduli, sidon_result, q_is_simple_rational).
+    """
+    results = []
+    
+    for q_den in range(2, max_q_den + 1):
+        for q_num in range(1, max_q_num + 1):
+            if gcd(q_num, q_den) != 1:
+                continue  # skip non-reduced fractions
+            if q_num == q_den:
+                continue  # skip q=1 (degenerate)
+            
+            moduli, q = select_qprofile_moduli(L0_base, q_num, q_den)
+            if moduli is None:
+                continue
+            
+            q_simple = is_simple_rational(q_num, q_den, max_den=7)
+            
+            embedded, M = embed(label_set, S, moduli)
+            sidon = sidon_check(embedded, moduli)
+            
+            entry = {
+                "q": str(q),
+                "q_float": float(q),
+                "q_num": q_num,
+                "q_den": q_den,
+                "q_simple_rational": q_simple,
+                "moduli": moduli,
+                "M": M,
+                "L0": moduli[0],
+                "L1": moduli[1],
+                "sidon_score": sidon["sidon_score"],
+                "collisions": sidon["collisions"],
+                "is_sidon": sidon["is_sidon"],
+                "total_pairs": sidon["total_pairs"],
+                "distinct_residues": sidon["distinct_residues"],
+            }
+            results.append(entry)
+    
+    return results
+
+
+# ── Main ──────────────────────────────────────────────────────────────────
+
+def run_experiment():
+    results = {
+        "experiment": "crt_qprofile_sweep",
+        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+        "config": {
+            "L0_base": 7,
+            "max_q_num": 30,
+            "max_q_den": 20,
+            "label_sets": [
+                {"name": "sidon_pow2", "labels": [1, 2, 4, 8, 16], "S": 32},
+                {"name": "sidon_singer5", "labels": [0, 1, 4, 14, 16], "S": 30},
+                {"name": "nonsidon_seq5", "labels": [0, 1, 2, 3, 4], "S": 5},
+            ],
+        },
+        "data": [],
+        "summary": {},
+    }
+    
+    label_sets = [
+        ("sidon_pow2", [1, 2, 4, 8, 16], 32),
+        ("sidon_singer5", [0, 1, 4, 14, 16], 30),
+        ("nonsidon_seq5", [0, 1, 2, 3, 4], 5),
+    ]
+    
+    L0_base = 7
+    
+    for desc, labels, S in label_sets:
+        print(f"\n{'='*60}")
+        print(f"  Label set: {desc} (n={len(labels)}, S={S})")
+        print(f"{'='*60}")
+        
+        sweep = sweep_q_profile(labels, S, L0_base)
+        
+        # Analyze
+        sidon_count = sum(1 for r in sweep if r["is_sidon"])
+        total_count = len(sweep)
+        q_simple_count = sum(1 for r in sweep if r["q_simple_rational"])
+        q_simple_sidon = sum(1 for r in sweep if r["q_simple_rational"] and r["is_sidon"])
+        q_not_simple_sidon = sum(1 for r in sweep if not r["q_simple_rational"] and r["is_sidon"])
+        
+        # q < 1 vs q > 1
+        q_lt1 = [r for r in sweep if r["q_float"] < 1.0]
+        q_gt1 = [r for r in sweep if r["q_float"] > 1.0]
+        q_lt1_sidon = sum(1 for r in q_lt1 if r["is_sidon"])
+        q_gt1_sidon = sum(1 for r in q_gt1 if r["is_sidon"])
+        
+        # Best q (highest Sidon score)
+        best = max(sweep, key=lambda r: r["sidon_score"]) if sweep else None
+        
+        summary = {
+            "total_configs": total_count,
+            "sidon_configs": sidon_count,
+            "sidon_rate": round(sidon_count / total_count, 4) if total_count > 0 else 0,
+            "q_simple_rational_count": q_simple_count,
+            "q_simple_sidon": q_simple_sidon,
+            "q_not_simple_sidon": q_not_simple_sidon,
+            "q_lt1_total": len(q_lt1),
+            "q_lt1_sidon": q_lt1_sidon,
+            "q_lt1_sidon_rate": round(q_lt1_sidon / len(q_lt1), 4) if q_lt1 else 0,
+            "q_gt1_total": len(q_gt1),
+            "q_gt1_sidon": q_gt1_sidon,
+            "q_gt1_sidon_rate": round(q_gt1_sidon / len(q_gt1), 4) if q_gt1 else 0,
+            "best_q": best["q"] if best else None,
+            "best_sidon_score": best["sidon_score"] if best else None,
+            "best_moduli": best["moduli"] if best else None,
+        }
+        results["summary"][desc] = summary
+        
+        print(f"  Total configs:  {total_count}")
+        print(f"  Sidon configs:  {sidon_count} ({summary['sidon_rate']:.1%})")
+        print(f"  q < 1: {q_lt1_sidon}/{len(q_lt1)} Sidon ({summary['q_lt1_sidon_rate']:.1%})")
+        print(f"  q > 1: {q_gt1_sidon}/{len(q_gt1)} Sidon ({summary['q_gt1_sidon_rate']:.1%})")
+        print(f"  q simple rational: {q_simple_sidon}/{q_simple_count} Sidon")
+        print(f"  q NOT simple:      {q_not_simple_sidon}/{total_count - q_simple_count} Sidon")
+        if best:
+            print(f"  Best q = {best['q']} (score={best['sidon_score']}, moduli={best['moduli']})")
+        
+        # Store all sweep data
+        for r in sweep:
+            r["label_set"] = desc
+            results["data"].append(r)
+    
+    content = json.dumps(results, indent=2, sort_keys=True, default=str)
+    results["sha256"] = hashlib.sha256(content.encode()).hexdigest()
+    return results
+
+
+def write_eval(results):
+    lines = [
+        "# CRT q-Profile Safety Factor Sweep",
+        "",
+        f"**Experiment:** {results['experiment']}",
+        f"**Date:** {results['timestamp']}",
+        f"**SHA-256:** `{results['sha256']}`",
+        "",
+        "## Summary: Sidon Rate by q-Regime",
+        "",
+        "| Label set | Total | Sidon | Rate | q<1 total | q<1 Sidon | q<1 rate | q>1 total | q>1 Sidon | q>1 rate | Simple q Sidon | Non-simple Sidon | Best q |",
+        "|-----------|-------|-------|------|-----------|-----------|----------|-----------|-----------|----------|----------------|------------------|-------|",
+    ]
+    for desc, s in results["summary"].items():
+        lines.append(
+            f"| {desc} | {s['total_configs']} | {s['sidon_configs']} | "
+            f"{s['sidon_rate']:.1%} | "
+            f"{s['q_lt1_total']} | {s['q_lt1_sidon']} | {s['q_lt1_sidon_rate']:.1%} | "
+            f"{s['q_gt1_total']} | {s['q_gt1_sidon']} | {s['q_gt1_sidon_rate']:.1%} | "
+            f"{s['q_simple_sidon']} | {s['q_not_simple_sidon']} | "
+            f"{s['best_q']} |"
+        )
+    
+    lines.extend([
+        "",
+        "## Predictions Tested",
+        "",
+        "1. **q < 1 (poloidal-dominated) should have higher Sidon rate than q > 1**",
+        "   - This is the toroidal/poloidal refinement prediction",
+        "   - If confirmed: poloidal resolution matters for Sidon structure",
+        "",
+        "2. **Simple rational q should have LOWER Sidon rate than non-simple q**",
+        "   - This is the R2 cross-pair coprimality prediction",
+        "   - Simple rationals = resonant surfaces = Sidon collapse",
+        "",
+        "3. **Best q should be < 1 and not a simple rational**",
+        "   - Optimal q-profile is poloidal-dominated and irrational",
+        "",
+    ])
+    
+    EVAL_PATH.write_text("\n".join(lines))
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("CRT q-Profile Safety Factor Sweep")
+    print("=" * 60)
+    
+    t0 = time.time()
+    results = run_experiment()
+    elapsed = time.time() - t0
+    
+    OUTPUT_PATH.write_text(json.dumps(results, indent=2, default=str))
+    print(f"\nResults → {OUTPUT_PATH}")
+    
+    write_eval(results)
+    print(f"EVAL → {EVAL_PATH}")
+    
+    print(f"\nElapsed: {elapsed:.1f}s")
+    print("=" * 60)
+    print("DONE")
+    print("=" * 60)