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arXiv Machine Learning

CopRoute: Dependence-Aware Conformal Routing for Language Models

CopRoute introduces a dependence-aware conformal routing method for language models that orders models by the number of new queries each correctly answers beyond those already covered, selecting the minimal cutoff that satisfies a target all‑miss risk. This approach accounts for model failure dependencies—such as shared training data or architecture—that traditional risk‑controlled policies ignore. Experiments on RouterBench and RouterEval show CopRoute achieves fewer certified inference calls than state‑of‑the‑art methods, reducing calls by up to 79 % in some cases while maintaining the same risk level.

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Opportunity Is Not Realizability: Selection-Valid Diagnostics for Multi-LLM Routing

arXiv:2608. 08265v1 Announce Type: new Abstract: Oracle routing measures how much a pool of language models could gain from per-query selection, but the diagnostic has two flaws: testing against a best fixed model selected on the same examples invalidates paired inference, and a full-information oracle sees outcomes no deployable router observes.

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Aug 25

Most of the LLM routing gap is task type

The paper investigates why large‑language‑model (LLM) routers—systems that select the best model for each query—often fail to outperform a single best model. By evaluating 14 models on 294 questions across seven task types and three languages, the authors find that a simple static mapping of task type to model improves 21 of the 29 questions that routing could potentially solve, and that learned routers do not significantly exceed this performance. The study highlights that most routing gains stem from task‑type specialization rather than complex learned decision rules.

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Agentic Routing: The Harness-Native Data Flywheel

arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.

By Xinchen Liu, Hang Zhou, Yingjie Zong, Yuchuan Tian, Liuyang Song, Shuo Zhang, Yulong Li, Wei He, Mengyu Zheng, Runke Liu, Siyang Cheng, Xiang Kuang, Hailin Hu, Kai Han, Yunhe Wang
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arXiv AI
Oct 2

False Floors: LLM Safety Routing Evaluations Break Under Distribution Shift

The paper examines safety routers—systems that route user requests to different language models—and finds that their performance degrades significantly when evaluated under distribution shift. In standard benchmarks, routers appear effective because the best single model is chosen from the same evaluation data, but when the data distribution changes, the routing advantage diminishes or disappears. The study quantifies this bias across multiple safety corpora, showing that routers offer little benefit under realistic shift conditions and that recognition‑based defenses can be undermined by attackers who know the model being used.

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