Closes the architect's tidy follow-up item on bench-number
methodology. The harness now accepts --runs N (default 1, byte-
identical output for back-compat); with N>=2 it reports
median + min..max per cell across runs, with N>=4 it drops the
slowest run before aggregating to match bench/run.sh's drop-
slowest throughput convention.
bench/run.sh now invokes the harness with --runs 5 for each of
the three latency arms, so a single bench/run.sh run produces
both a regression-stable throughput table and a regression-stable
latency table.
The qualitative claims from JOURNAL 2026-05-08 (RC tail latency
23x better than Boehm; RC RSS lower than Boehm) hold at the new
stat-of-5 confidence level — a 5-run smoke on the explicit-rc arm
shows p99=296µs (range 289-311), p99/median=1.31x (range 1.28-1.37).
Variance is well below the signal.
The latency_harness.py harness spawns the bench binary on a PTY,
records monotonic_ns() per stdout line, and reports inter-arrival
gap distribution (median / p99 / p99.9 / max). Tail latency is
Decision 10's central real-time claim; total wall-time and RSS are
the wrong metrics for that question.
Paired fixtures: bench_latency_implicit (Boehm-fair, no mode
annotations, leaks under --alloc=rc) and bench_latency_explicit
(mode-annotated hot path, what RC was built for). Both use a
depth-19 balanced tree (~16 MB) as the persistent live working
set, plus per-op IntList build+sum churn forcing GC pressure
under Boehm.
Authored by ailang-bencher; ships evidence, not features.
Brummel