arXiv AI By Liang He, Jingbo Wen, Yixiong Chen, Yue Yang, Qizhen Lan, Kangning Cui, Xilu Wang

You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference

Read the original on arXiv AI →

The paper demonstrates that in open‑weight LLM inference markets, selecting a model is insufficient; clients must also choose a provider, as the same model can differ markedly in quality, latency, availability, and price across providers. The authors propose a market‑aware routing approach, including a measured‑map policy and an online router called FACET, which certifies provider feasibility for each task and safely falls back to a reliable anchor. Experiments show that this strategy yields cost savings while maintaining quality and avoiding degraded endpoints.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
2d ago

TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing

arXiv:2605.18859v3 Announce Type: replace-cross Abstract: LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single u...

By Pei Yang, Wanyi Chen, Tongyun Yang, Pengbin Feng, Jiarong Xing, Wentao Guo, Yuhang Yao, Yuhang Han, Hanchen Li, Xu Wang, Zeyu Wang, Jie Xiao, Anjie Yang, Liang Tian, Lynn Ai, Eric Yang, Tianyu Shi
arXiv AI
Sep 24

Learning the Cost of Reliable Inference

arXiv:2609.28322v1 Announce Type: new Abstract: Benchmarking and routing platforms increasingly act as intermediaries connecting large language model providers with end-users. However, providers on t...

By Dimitrios Rontogiannis, Ander Artola Velasco, Manuel Gomez Rodriguez
arXiv Machine Learning
3d ago

HybridInfer: Thermal-Aware Reinforcement-Learning Tier Routing for On-Device, Edge, and Cloud LLM Inference

HybridInfer is a thermal‑aware reinforcement‑learning router that selects among on‑device, edge, and cloud large language model tiers based on a phone’s thermal headroom and query complexity. Trained offline with a Q‑learning policy, it balances quality, latency, cost, and a thermal penalty, and includes a locality bonus that encourages on‑device execution. In real Android tests on 210 prompts, the learned router outperformed two hand‑tuned heuristics in quality while maintaining the lowest cost, and it proved more reliable and faster than always‑on‑device inference, especially for long queries.

By Simran Koul
arXiv AI
Aug 21

Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation

arXiv:2608. 20316v1 Announce Type: new Abstract: Heterogeneous AI systems composed of multiple models, architectures, harnesses, or inference-time settings can improve quality and efficiency by routing queries to the specialist who can answer most effectively at the lowest cost.

By Adam Fisch, Shubhendu Trivedi, Fantine Huot, William W. Cohen, Michael Kaisers, Mirella Lapata, Kate Larson, Jacob Eisenstein
arXiv Machine Learning
Jul 30

Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents

arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.

By Yicheng Feng, Yan Zhang, Yan Cheng, Wei Qi