arXiv:2511. 09373v2 Announce Type: replace-cross Abstract: LLMs now tackle a wide range of software-related tasks, yet we show that their performance varies markedly both across and within these tasks.
By Adam \v{S}torek, Vikas Upadhyay, Marianne Menglin Liu, Daniel W. Peterson, Anshul Mittal, Sujeeth Bharadwaj, Fahad Shah, Sujith Ravi, Dan Roth
The paper introduces SaveRouter, a sparse‑supervision framework for large language model routing that selectively gathers informative model feedback and shares capability information across related queries. By using only about 33–41% of available training feedback, SaveRouter achieves competitive or superior routing quality while reducing the break‑even deployment volume by 1.9–9.5× compared to conventional routers. The study also shows that the supervision level that minimizes serving cost may differ from the one that yields the earliest payback.
By Guannan Lai, Gelin Bian, Hao-Xuan Ma, Jun-Peng Jiang, Long Chen, Jian-Dong Liu, Zhi-Hao Tan, Han-Jia Ye
The paper introduces VDAR-Router, a routing framework for large language models that uses verbalized query difficulty analysis to guide model selection. It first generates an explicit difficulty profile for each query, retrieves historical examples with similar profiles, and then estimates model suitability to choose a model based on a reward function balancing performance and cost. Experiments on three datasets show that VDAR-Router consistently outperforms existing baselines in cost‑performance trade‑offs, and case studies confirm that explicit difficulty analysis improves example relevance and routing reliability.
By Yu-Chien Tang, Jun-Chen Hung, Wen-Chih Peng, An-Zi Yen
arXiv:2603.04445v3 Announce Type: replace-cross
Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mo...
By Yasmin Moslem, John D. Kelleher
arXiv:2609.23085v1 Announce Type: cross
Abstract: Large language models (LLMs) and agentic AI systems are creating rapidly growing inference energy demands as model sizes grow and reasoning trajector...
By Muhammad Abdur Rab Siddiqui, Daniela Rojas, Chen Yang, Wenqi Cui, Yuanyuan Shi, Yize Chen
The paper "Towards Optimizing SQL Generation via LLM Routing" proposes a routing approach for Text-to-SQL tasks that dynamically selects the most cost‑effective large language model (LLM) for each query. Two routing strategies—score‑based and classification‑based—are introduced, achieving accuracy comparable to the best LLM while reducing latency and monetary cost. The authors design the routers for easy training and efficient inference, and demonstrate a practical accuracy‑cost trade‑off on the BIRD dataset.
By Mohammadhossein Malekpour, Nour Shaheen, Foutse Khomh, Amine Mhedhbi