How Much of the Routing Gap Is Real? Decomposing the Router-to-Oracle Gap into Reproducible Specialist Advantage and Single-Draw Label Noise
On real open-model pools, 12--36% of the reported router-to-oracle gap is single-draw label noise that no single-commit router can capture, while the majority is genuine, recoverable specialist advantage; this work proves why (a recoverability asymmetry) and releases a protocol to measure it. Routing among large language models (LLMs) trades cost for quality, motivated by the gap between learned routers and a per-instance oracle. But under stochastic decoding that oracle is a single Bernoulli draw, not a reproducible property. We recast the question structurally: the expected oracle decomposes as $O^{\exp}=O^{\mathrm{repro}}+Δ$, into reproducible single-commit headroom $O^{\mathrm{repro}}$ and a non-negative single-commit selection floor $Δ$. Our main result is a recoverability asymmetry: this floor is closed by no single-commit router (deterministic or randomized), yet is provably recovered by test-time sampling: best-of-$K$ on the committed model, at the oracle's own budget, dominates the independent-pool single-draw oracle. This cap needs no cross-model independence, pinning "not recoverable" to single-commit selection, not to information. The floor's magnitude is a prospective, conservative localization, not an audit: LLMRouterBench (33 models, 391,645 instances) builds its oracle as a per-query union of single $T=0.2$ draws, so its 20-point gap is by construction a union of stochastic draws; since $O^{\mathrm{repro}}$ is non-identifiable at $k=1$, we re-estimate by fresh $k\ge20$ resampling under one-sided, dependence-corrected bounds. Across three controlled open-model re-generations (arithmetic, competition math, and non-math science), single-draw noise is a substantial minority of the gap, larger on unsaturated benchmarks and approaching half on the hardest queries. We release a multi-sample oracle protocol that routing benchmarks can adopt.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Flexible Routing via Uncertainty Decomposition
A key strategy for balancing performance and cost in modern machine learning systems is to dynamically route queries to either a low-cost model or a more expensive oracle (such as a large pretrained model or human expert…
LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing
Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LLM routing. It comprises over 400K instan…
The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers
LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by dynamically selecting a model for each query. Recent work has explored a broad range of routing methods, including cluste…
VL-RouterBench: A Benchmark for Vision-Language Model Routing
Multi-model routing has evolved from an engineering technique into essential infrastructure, yet existing work lacks a systematic, reproducible benchmark for evaluating vision-language models (VLMs). We present VL-Router…
PROTEUS: SLA-Aware Routing via Lagrangian RL for Multi-LLM Serving Systems
Production LLM deployments serve diverse workloads where cost and quality requirements vary by customer tier, time of day, and query criticality. Model serving systems accept latency SLOs directly. LLM routers do not. Th…