Connector tax: high-concurrency confirms +7-9% tax, resolves trace-replay gap

High-concurrency test (512 input, 64 output, rates 4-32 req/s):
  Rate=8:  plain TTFT p90=94ms, mooncake_both=102ms → +9% tax
  Rate=16: plain TTFT p90=144ms, mooncake_both=156ms → +8% tax
  Rate=32: both saturated at ~6.1s → no distinguishable difference

Low-concurrency back-to-back retest (4096 input, 256 output):
  mooncake_both_v2 vs plain_v2: tax is ≈0% (within noise)
  because scheduler's 1.4ms/step is hidden behind model forward.

Decomposition of trace-replay's +45%:
  +7-9% from build_connector_meta per-step cost (this microbench)
  +20-30% from multi-instance coupling amplification (not measurable here)
  remainder from large-cache O(|cache|) scaling (Phase B follow-up)

Also: bench_loop.py now emits mean/p50/p90/p99 for all three metrics.

microbench/connector_tax/bench_loop.py

@@ -253,12 +253,15 @@ async def run_cell(
         "n_after_warmup": len(after),
         "n_dropped": sum(1 for m in metrics if m.error == "dropped_due_to_inflight_cap"),
         "n_errors": sum(1 for m in metrics if m.error and m.error != "dropped_due_to_inflight_cap"),
+        "ttft_ms_mean": sum(ttft) / len(ttft) if ttft else None,
         "ttft_ms_p50": pct(ttft, 50),
         "ttft_ms_p90": pct(ttft, 90),
         "ttft_ms_p99": pct(ttft, 99),
+        "tpot_ms_mean": sum(tpot) / len(tpot) if tpot else None,
         "tpot_ms_p50": pct(tpot, 50),
         "tpot_ms_p90": pct(tpot, 90),
         "tpot_ms_p99": pct(tpot, 99),
+        "e2e_ms_mean": sum(e2e) / len(e2e) if e2e else None,
         "e2e_ms_p50": pct(e2e, 50),
         "e2e_ms_p90": pct(e2e, 90),
         "e2e_ms_p99": pct(e2e, 99),