Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks

ArXi:2605.24217v1 Announce Type: new As Large Language Models (LLMs) transition from research environments to production deployments, evaluating their performance against strict Service Level Objectives (SLOs) has become critical. However, current evaluation methodologies suffer from severe measurement bias at scale. We nstrate that widely used benchmarking utilities rely on single-process, asyncio-driven architectures that