AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

Abstract:Large language model agents tune GPU kernels and serving engines through a closed loop of propose, measure, and keep, but the measurements behind this loop are not trustworthy. We characterize four failure modes from a four-day pilot corpus of 619 model calls: strawman baselines manufacture speedups, absolute times do not transfer across machines, saturated tasks nullify comparisons, and infrastructure defects impersonate science. We present AutoTuneBench, a benchmark and measurement protocol that makes trust architectural. The protocol is frozen as code with test-enforced provenance; a database-level validator rejects out-of-protocol results; anti-cheat checks run outside the agent's modification surface; comparisons follow pre-registered readouts; and measurements anchor to externally published results, grounded in paired-seed statistics with a 5\% cross-run coefficient-of-variation cap. Honest measurement rewrites the headlines: our best kernel reads 10.6x against a naive baseline but 2.03x against the honest one; one configuration delivers 1.174x on one machine and 1.0049x on another; a pre-registered on/off comparison nulls at a shared wall (2.4840 vs 2.4957\,ms); and the KernelBench Level-1 suite admits 51\% of tasks with median speedup 1.0001x over PyTorch eager. The protocol, the two-engine corpus (vLLM and SGLang), and its audit trail are released as open artifacts.

15 pages, 7 tables, 6 figures. Code and measurement corpus: this https URL (tag v1.0.0)

Artificial Intelligence (cs.AI)

arXiv:2609.18123 [cs.AI]

(or arXiv:2609.18123v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.18123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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