Finding
Two Sep 3 frontier results expose one benchmark failure class.
- arXiv:2609.04198 reports shared endpoint observer repeatability far below preregistered gates despite clean engineering. Same window ranking agreement was reported at Spearman 0.400 versus a required 0.90, with byte identical next day replay at 0.78 versus 0.99.
- arXiv:2609.03966 reports interface induced trajectory censoring where valid emitted tool calls can be lost by the serving template and parser interaction, producing zero observed tool activity despite capable weights.
RuV impact
MetaHarness, Dream Machine, Ruflo, Cognitum routing, RVM security evaluation, Core Memory experiments, and RuVector benchmarks can all optimize against an instrument that is not stable enough to certify the candidate.
Proposed primitive
Add a dependency free InstrumentQualificationReceipt in @metaharness/bench with two independent checks:
- repeat ranking reliability using deterministic Spearman correlation across matched repetitions
- interface preservation from well formed emitted tool call to parsed call to executed call
The receipt must fail closed on insufficient samples, malformed rankings, non finite values, duplicate identities, or silent interface loss. Output is evidence only with authority: none.
Promotion gates
No benchmark may use the instrument as promotion evidence unless explicit policy thresholds pass. Defaults are not silently invented by the library.
Minimum reproduction program:
- stable synthetic control
- unstable ranking control
- parser censorship control where emitted valid calls are not parsed
- partial execution control
- malformed input and resource bound tests
- one real MetaHarness host plus one shared model endpoint
Report model and endpoint identifiers, adapter and template versions, seeds, sample size, correlations, call counts, latency, cost, failures, and reproduction steps.
Governance
Research, implementation, adversarial review, security, testing, reproducibility, and release roles are independent. No autonomous merge, deployment, credential changes, or evaluator threshold mutation after outcomes are observed.
Acceptance
A candidate implementation must detect every synthetic silent censorship case, reject insufficient or malformed reliability evidence, preserve stable controls, add zero runtime dependencies, keep existing bench behavior unchanged, and pass repository CI and security gates.
Finding
Two Sep 3 frontier results expose one benchmark failure class.
RuV impact
MetaHarness, Dream Machine, Ruflo, Cognitum routing, RVM security evaluation, Core Memory experiments, and RuVector benchmarks can all optimize against an instrument that is not stable enough to certify the candidate.
Proposed primitive
Add a dependency free
InstrumentQualificationReceiptin@metaharness/benchwith two independent checks:The receipt must fail closed on insufficient samples, malformed rankings, non finite values, duplicate identities, or silent interface loss. Output is evidence only with
authority: none.Promotion gates
No benchmark may use the instrument as promotion evidence unless explicit policy thresholds pass. Defaults are not silently invented by the library.
Minimum reproduction program:
Report model and endpoint identifiers, adapter and template versions, seeds, sample size, correlations, call counts, latency, cost, failures, and reproduction steps.
Governance
Research, implementation, adversarial review, security, testing, reproducibility, and release roles are independent. No autonomous merge, deployment, credential changes, or evaluator threshold mutation after outcomes are observed.
Acceptance
A candidate implementation must detect every synthetic silent censorship case, reject insufficient or malformed reliability evidence, preserve stable controls, add zero runtime dependencies, keep existing bench behavior unchanged, and pass repository CI and security gates.