Strong write-up. The anchor-set point is the part I’d underline: once the judge is also a model, the harness needs its own calibration surface, not just a better judging prompt.
One enhancement I’d consider is making the eval receipt explicit for every run: judge version, candidate model, rubric version, anchor-set agreement, order-shuffle result, drift signal, and which failures were deterministic versus judgment-based. That makes the dashboard more inspectable when a score changes.
Affiliation note: I’m with nxus.SYSTEMS. This overlaps with nxusKit SDK CE examples around model-research harnesses, structured output, deterministic checks, Bayesian confidence, and retry/fallback patterns. CE is always free.
The easiest way to find the examples is to search for: nxusKit SDK examples
Strong write-up. The anchor-set point is the part I’d underline: once the judge is also a model, the harness needs its own calibration surface, not just a better judging prompt.
One enhancement I’d consider is making the eval receipt explicit for every run: judge version, candidate model, rubric version, anchor-set agreement, order-shuffle result, drift signal, and which failures were deterministic versus judgment-based. That makes the dashboard more inspectable when a score changes.
Affiliation note: I’m with nxus.SYSTEMS. This overlaps with nxusKit SDK CE examples around model-research harnesses, structured output, deterministic checks, Bayesian confidence, and retry/fallback patterns. CE is always free.
The easiest way to find the examples is to search for: nxusKit SDK examples