Version 1.0: VLAW parameterization + detector cascade

- Implemented exact ln/exp2 infrastructure (log2_ln.hpp/cpp)
- Parameterized VLAW α/β/c by (fc, q, sens) configuration
- Implemented real RFFT for FIR construction
- Fixed VLAW parameterization for dual group (3.455 → 0.764 dB)
- Added detector cascade 529c60 (Haar smoothing, magnitude, peak processing)
- TOTAL error: 0.870 dB (vs bridge baseline 1.594 dB)

Results:
- t1kq: 0.618 dB (bridge: 0.226 dB)
- t1k: 0.938 dB (bridge: 1.801 dB) ✓ better
- al: 0.727 dB (bridge: 0.638 dB)
- res: 0.284 dB (bridge: 0.628 dB) ✓ better
- dual: 0.764 dB (bridge: 0.726 dB)
- comb: 3.000 dB (bridge: 10.149 dB) ✓ better

scripts/quick_vlaw_grid.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python3
+"""
+Quick VLAW parameter grid search - test fewer combos per case.
+"""
+
+import numpy as np
+import os
+import sys
+import subprocess
+import json
+
+sys.path.insert(0, '/home/m/re-tools/scripts')
+import corpus
+
+corpus.RB = '/home/m/re-tools/dsp/build/render48k'
+
+with open('scripts/baseline_bridge.json') as f:
+    REF_ERRORS = json.load(f)
+
+def structural_cases():
+    out = []
+    for name, inp, args, ref, f in corpus.build_cases():
+        joined = [','.join(args)] if len(args) == 3 else args
+        out.append((name, inp, joined, ref, f))
+    return out
+
+def run_vlaw(inp, args, alpha, beta, c, delta):
+    out = f'/tmp/vlaw_{alpha}_{beta}_{c}_{delta}_{os.path.basename(inp)}.wav'
+    env = {
+        **os.environ,
+        'RT_VLAW': '1',
+        'RT_VLAW_ALPHA': str(alpha),
+        'RT_VLAW_BETA': str(beta),
+        'RT_VLAW_C': str(c),
+        'RT_VLAW_DELTA': str(delta),
+        'RT_SYN': '1', 'RT_NOWARP': '1', 'RT_NOIIR3': '1', 'RT_IIR12': '0',
+    }
+    subprocess.run([corpus.RB, inp, out] + args, capture_output=True, env=env, cwd='/home/m/re-tools')
+    return out
+
+def eval_error(out, ref, f):
+    if not os.path.exists(out) or os.path.getsize(out) == 0:
+        return None
+    ref_sig = corpus.load_mono(ref)
+    out_sig = corpus.load_mono(out)
+    min_len = min(len(ref_sig), len(out_sig))
+    ref_sig = ref_sig[-min_len:]
+    out_sig = out_sig[-min_len:]
+    ref_ta = corpus.ta(ref_sig, f)
+    out_ta = corpus.ta(out_sig, f)
+    return corpus.db(out_ta / ref_ta)
+
+def group_key(name):
+    return name.split('_')[0]
+
+all_cases = structural_cases()
+groups = {}
+for name, inp, args, ref, f in all_cases:
+    g = group_key(name)
+    groups.setdefault(g, []).append((name, inp, args, ref, f))
+
+# Pick one case per group
+rep_cases = {}
+for g in ['t1kq', 't1k', 'al', 'res', 'dual']:
+    if g in groups:
+        # Pick middle-ish case
+        cases = groups[g]
+        rep_cases[g] = cases[len(cases)//2]
+
+print("Representative cases:")
+for g, (name, inp, args, ref, f) in rep_cases.items():
+    print(f"  {g}: {name}")
+
+# Test a small grid around dual params
+dual_params = (3.2193, 0.4927, 0.5423, 6.9177)
+
+print("\n=== Grid search per group ===")
+results = {}
+
+for g, (name, inp, args, ref, f) in rep_cases.items():
+    print(f"\n--- {g} ({name}) ---")
+    best = None
+    best_err = float('inf')
+    
+    # Coarse grid
+    alphas = np.linspace(1.0, 5.0, 5)
+    betas = np.linspace(0.2, 0.8, 5)
+    cs = np.linspace(-0.5, 2.0, 5)
+    deltas = np.linspace(0.0, 12.0, 5)
+    
+    for alpha in alphas:
+        for beta in betas:
+            for c in cs:
+                for delta in deltas:
+                    out = run_vlaw(inp, args, alpha, beta, c, delta)
+                    err = eval_error(out, ref, f)
+                    if err is not None and abs(err) < best_err:
+                        best_err = abs(err)
+                        best = (alpha, beta, c, delta, err)
+                        print(f'  {name}: α={alpha:.3f}, β={beta:.3f}, c={c:.3f}, Δ={delta:.3f} => {err:.3f} dB')
+    
+    if best:
+        results[g] = best[:4]
+        print(f'  BEST {g}: α={best[0]:.4f}, β={best[1]:.4f}, c={best[2]:.4f}, Δ={best[3]:.4f} => {best[4]:.3f} dB')
+
+print("\n=== SUMMARY ===")
+for g, (a, b, c, d) in results.items():
+    print(f'{g}: α={a:.4f}, β={b:.4f}, c={c:.4f}, Δ={d:.4f}')
+
+with open('/tmp/opencode/vlaw_params.json', 'w') as f:
+    json.dump({g: {'alpha': a, 'beta': b, 'c': c, 'delta': d} 
+               for g, (a, b, c, d) in results.items()}, f, indent=2)
+print('\nSaved to /tmp/opencode/vlaw_params.json')