Two reported MMLU accuracies - 0.781 for build 42 and 0.79 for build 44 - claim the same provider, model family and benchmark name but resolve to different content-addressed frames, so an APL verifier returns “incomparable” for a score-delta query. The two records use identical score predicates and metric labels, but their frame hashes point to different procedure and scope: different dataset splits, graders, runners and environment permissions. Because APL ties a claim to a specific hashed frame, the numeric label alone (mmlu) does not establish comparability; the verifier’s output depends on the full, referenced frame, and apl-valid checks only structural conformance, not factual correctness.
The piece breaks down five variables that the shared benchmark name leaves open and quantifies their real effects: split (dev vs test-lite; differing denominators), implementation/harness (HELM vs other harnesses showing multi-point differences and rank flips), prompt format and answer position (documented multi-percent to multi-tens-of-points swings), grader choice (exact-match vs LLM judge with known biases), and runner reachability/network access (post-hoc contamination causing ~15-point drops on subsets). A 2026 survey highlights widespread missing reproducibility fields in published results. The conclusion: comparability is a property of the referenced frame, not of the benchmark label or the bare number.
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