Evidence record

MLE-bench

Original paper v1 (2024) · Claude 3.5 Sonnet / AIDE · 2024-10-09

Reported result · numeric

7.6 ± 1.8%

Any medal rate · percent

benchmark author reported · extraction review: agent checked

Source and extraction

Published 2024-10-09

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering (v1)Section 3.1, Table 2Original source ↗

Evaluation setup

task count	75
run count	3
aggregate method	Mean across repeated attempts; one SEM
wall clock budget	24 hours per run
hardware	36 vCPUs, 440 GB RAM, one Nvidia A10 GPU
scaffold	AIDE
comparability caveats	Number of seeds differs; comparison explicitly reported by authors.

Not reported: split, task snapshot, attempts per task, selection rule, token budget, training budget, inference budget, monetary cost, tool access, internet access, filtering, evaluator version, human intervention, task exclusions, contamination concerns.

Comparability

direct comparison

Different seed counts affect precision; no significance inference from rounded means.

Limitations

Revised 2026 tasks and percentile scoring are incompatible with 2024 medal rates.
Public competition history creates contamination risk.