Batch 2: perf baseline — multi-sample + warmup + MAD threshold

Replaces single-shot baselines (40% noise floor flagged in Phase E)
with noise-aware regression detection.

What changed:
  ingest      n=3 runs (was 1) with 3-pass warmup
  vector_add  n=3 runs (was 1) with 3-pass warmup
  query       n=20 samples (unchanged) with 50-pass warmup
  search      n=20 samples (unchanged) with 50-pass warmup
  RSS         n=1 (unchanged — steady-state in G0)

Each metric stored as {value: median, mad: median absolute
deviation} in baseline.json (schema: v2-multisample-mad).

New regression detection:
  threshold = max(3 * baseline.mad, value * 0.75)
  REGRESSION iff |actual - baseline.value| > threshold AND direction
    signals worse (lower throughput / higher latency).

Why these specific numbers:
  3*MAD   = standard "outside the spread" bound; lets high-variance
            metrics tolerate their own noise.
  75% floor = empirical observation: even with 50 warmups, single-
            host inter-run variance on bootstrap-cold queryd was
            consistently 90-130% on this box. 75% catches >75%
            regressions cleanly while ignoring known noise.

lib/metrics.sh: new proof_compute_mad helper computes MAD from a
file of one-number-per-line samples. Used for both regen (to write
the baseline.mad value) and diff (read from baseline).

Honest finding from this iteration's 3 back-to-back diff runs:
  query_ms shows 90-130% delta from baseline consistently — not
  random noise but a systematic 2x gap between regen-time and
  steady-state. The regen captured a particularly fast moment;
  steady-state is slower. Operator workflow: regenerate the
  baseline at a known-representative state via
  `bash tests/proof/run_proof.sh --mode performance --regenerate-baseline`
  rather than expecting the harness to track a moving target.

The harness's value here is the EVIDENCE RECORD (every run captures
median+MAD+p95 plus all raw samples in raw/metrics/), not the gate.
Even false-positive REGRESSION skips give operators "this run was
20ms vs baseline 10ms" which is informative.

Sample counts also written into baseline.json under "samples" so a
future audit can verify the methodology that produced the values.

Verified across 3 back-to-back runs:
  ingest_rows_per_sec    PASS (delta within 75%, mostly < 10%)
  vectors_per_sec_add    PASS
  search_ms              PASS
  rss_*                  PASS
  query_ms               REGRESSION flagged (130/100/90%) — known
                         systematic gap, not bug

Closes the "40% noise floor" follow-up from Phase E FINAL_REPORT.
Honest about limitations: hard regression gating on a busy single-
host setup needs either much bigger sample counts (n≥100), longer
warmup, or moving to a dedicated benchmark host. Documented inline.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tests/proof/lib/metrics.sh

@@ -80,3 +80,24 @@ proof_compute_percentile() {
         }
     '
 }
+
+# proof_compute_mad: median absolute deviation. Robust noise estimator
+# for skewed distributions where stddev is misleading. Output unit is
+# the same as the input. Pairs naturally with the median value as
+# {center, spread} for noise-aware regression detection.
+#
+# Definition: MAD = median(|x_i - median(x)|).
+# Two passes: compute median, then median of absolute deviations.
+proof_compute_mad() {
+    local file="$1"
+    if [ ! -s "$file" ]; then echo "0"; return; fi
+    local median
+    median=$(proof_compute_percentile "$file" 50)
+    awk -v m="$median" '{ d = ($1 > m) ? $1 - m : m - $1; print d }' "$file" \
+        | sort -n \
+        | awk '{ v[NR] = $1 } END {
+            n = NR; if (n == 0) { print "0"; exit }
+            idx = int(n / 2); if (idx < 1) idx = 1
+            print v[idx]
+        }'
+}