Routing a query through an appropriate LLM is challenging, particularly when user preferences are expressed in natural language and model attributes are only partially observable. We propose a constraint-based interpretation of language-conditioned LLM routing, formulating it as a weighted MaxSAT/MaxSMT problem in which natural language feedback induces hard and soft constraints over model attributes. Under this view, routing corresponds to selecting models that approximately maximize satisfaction of feedback-conditioned clauses. Empirical analysis on a 25-model benchmark shows that language feedback produces near-feasible recommendation sets, while no-feedback scenarios reveal systematic priors. Our results suggest that LLM routing can be understood as structured constraint optimization under language-conditioned preferences.
MaxSAT-Based Feedback for Guiding Vision-Language Models in Sudoku
Artificial Intelligence Research Institute (IIIA), Consejo Superior de Investigaciones Científicas (CSIC)（人工智能研究所（IIIA），西班牙科学研究高级理事会（CSIC））；Institut de Robòtica i Informàtica Industrial (IRI-CSIC-UPC)（工业机器人与信息学研究所（IRI-CSIC-UPC））
Reliable Reasoning with Large Language Models via Preference-Based Maximum Satisfiability
Artificial Intelligence Research Institute (IIIA) Consejo Superior de Investigaciones Científicas (CSIC)（人工智能研究所（IIIA）西班牙国家科学研究委员会（CSIC））；Department of Computer Science University of Oxford（计算机科学系牛津大学）；Institut de Robòtica i Informàtica Industrial (IRI-CSIC-UPC)（机器人与信息工业研究所（IRI-CSIC-UPC））
Plan before Solving: Problem-Aware Strategy Routing for Mathematical Reasoning with LLMs
School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；MOE KLINNS Lab, Xi’an Jiaotong University（西安交通大学MOE KLINNS实验室）；Shaanxi Province Key Laboratory of Big Data Knowledge Engineering（陕西省大数据知识工程重点实验室）；Sydney AI Centre, The University of Sydney（悉尼大学悉尼人工智能中心）
ARMOR-MAD: Adaptive Routing for Heterogeneous Multi-Agent Debate in Large Language Model Reasoning
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；School of Artificial Intelligence, Shenzhen Technology University（深圳技术大学人工智能学院）
ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference
Nanyang Technological University（南洋理工大学）；Carnegie Mellon University（卡内基梅隆大学）；Northeast University (Qinhuangdao Campus)（东北大学（秦皇岛校区））；Beihang University（北航）；University of the Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）；Sun Yat-sen University（中山大学）
MMR-Bench: A Comprehensive Benchmark for Multimodal LLM Routing
School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）；National Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家实验室，南京大学）
MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT 06269（机械、航空航天与制造工程学院，康涅狄格大学，斯托尔斯，CT 06269）；Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA（机械工程系，麻省理工学院，剑桥，MA 02139，美国）
CURE: Confidence-driven Unified Reasoning Ensemble Framework for Medical Question Answering
School of Information Technology and Computer Science, Nile University（信息科技与计算机科学学院，尼罗大学）；Graduate School of Information Science, University of Hyogo（信息科学研究生院，播磨大学）；Advanced Medical Engineering Research Institute, University of Hyogo（先进医疗工程研究所，播磨大学）
Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing
School of Computer Science, Peking University（北京大学计算机科学学院）；MOE Key Laboratory of Computational Linguistics, Peking University（北京大学教育部计算语言学重点实验室）；National Key Laboratory for Multimedia Information Processing, Peking University（北京大学国家多媒体信息处理重点实验室）
DyTopo: Dynamic Topology Routing for Multi-Agent Reasoning via Semantic Matching
Peking University, Beijing, China（北京大学）；Georgia Institute of Technology, Atlanta, United States（佐治亚理工学院）；Southeast University, Location, Country（东南大学）；Tsinghua University（清华大学）
