Improving LLM Performance Through Black-Box Online Tuning: A Case for Adding System Specs to Factsheets for Trusted AI

arXiv cs.AI / 3/13/2026

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Key Points

The paper proposes a black-box online controller that uses only end-to-end measurements over short segments and hill climbing to maximize goodput for LLM serving without internal instrumentation.
It provides empirical evidence that this black-box online approach is well-founded and effective in practice.
The method is demonstrated on LLM serving as a concrete example and shows potential throughput gains while meeting service-level objectives.
The authors argue for integrating system performance and sustainability metrics into Factsheets to help organizations adopting AI systems.

Abstract

In this paper, we present a novel black-box online controller that uses only end-to-end measurements over short segments, without internal instrumentation, and hill climbing to maximize goodput, defined as the throughput of requests that satisfy the service-level objective. We provide empirical evidence that this design is well-founded. Using this advance in LLM serving as a concrete example, we then discuss the importance of integrating system performance and sustainability metrics into Factsheets for organizations adopting AI systems.