20/20 sessions: A/A false-positive rate 0.000 (<0.05 required),
injected-5%-slowdown detection 1.000 (>=0.95 required). The C workload
is a trusted measurement anchor; verdict benches on cjson are now valid.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
Full-protocol calibration on a GitHub-hosted runner: A/A false-positive
0/20, injected-5%-slowdown detection 20/20, per-session CIs transcribed
with run provenance (artifacts expire; the md is the durable record).
With the local-container anchor and gates running end-to-end on both
targets in CI, all SPEC §5 Phase 1 exit criteria are met on free
infrastructure. CLAUDE.md phase line moved to Phase 2 (agent);
dedicated pinned hardware remains listed in docs/infra.md as the bar
for Phase 3/4 customer-facing case studies.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
Adds 'bench-runner calibrate': N A/A sessions counted against a <5%
false-positive bar, and N sessions with a synthetic slowdown injected
as a real busy-wait inside the candidate's timed window, counted
against a >=95% detection bar (detected = ratio CI entirely below 1.0).
Bootstrap seed varies per session so resampling noise is not
correlated across the very statistic being validated. JSON evidence
includes per-session CIs, injected wait, acceptance verdict, and env
fingerprint.
First run against the real comrak workload (20+20 sessions, pinned
core): false-positive rate 0.000, detection rate 0.950 -> PASS.
Evidence: results/calibration/2026-07-06-comrak-full-calibration.json.
Closes the SPEC 3.1 acceptance-criteria gap; remaining Phase 1 gaps:
no-net containers, perf profiling, diff-test orchestration, agent IPC.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
Commit the spec and scaffold the system it describes:
- SPEC.md: the full v0.1 engineering spec
- crates/: trust-layer Rust workspace, unit-tested
- bench-runner: interleaved ABAB scheduling with discarded warm-ups,
deterministic bootstrap CIs over the ratio of medians,
CI-lower-bound accept rule, env fingerprinting, and a CLI with
compare + A/A self-test modes (A/A verified passing end-to-end)
- diff-test: gate outcome model, sha256 corpus pin verifier that
refuses on mismatch, per-target equivalence policy parser
(byte-identical default, explicit fp-tolerance opt-in)
- ledger: append-only SQLite attempt schema with UPDATE/DELETE
refused by triggers, anti-double-attempt query
- report: cores-saved and dollars/yr ROI math over speedup CIs,
pricing.toml inputs
- playbook/: classes 1-7 with preconditions, procedure, verification
notes, and known failure modes per class
- agent/: Python package skeleton — six-tool contract and prompting
spine, harness IPC deferred to Phase 1
- config/: accept.toml (2% threshold, 30 runs/side), pricing.toml
- targets/, corpora/, results/calibration/: layout and policy docs
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
Claude