With the rapid development of large language models (LLMs), role-playing dialogue systems have become increasingly popular for immersive and personalized human–AI interaction. However, in real-world role-playing scenarios, users often provide lazy queries—brief and ambiguous responses such as "oh," "hmm," or "?"—which lack explicit intent and emotional cues, making it difficult for role-playing LLMs to generate engaging replies. Existing query rewriting methods mainly target functional tasks such as information retrieval and are not tailored to role-playing dialogue, where rewrite quality depends on role-playing-specific factors such as character consistency and personalized user preferences. To address this, we first propose a listwise Generative Reward Model (GRM) trained from real user preference data collected through online regeneration behavior, which provides fine-grained process and final preference signals for training a Lazy Query Rewriter. Using rejection fine-tuning and reinforcement learning, the LQ-Rewriter learns to transform underspecified user inputs into richer expressions while preserving user intent and dialogue context. Experiments on real-world dialogue logs from a mainstream Chinese AI-powered romantic role-playing platform show that our GRM substantially outperforms general-purpose LLM judges. Moreover, our LQ-Rewriter achieves the highest GRM-based and human preference win rates among all rewriting baselines, and brings tangible improvements in online deployment.

Large language models (LLMs) have demonstrated exceptional performance across a wide range of natural language tasks. However, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost. While powerful models deliver better results, they come at a high cost, whereas smaller models are more cost-effective but less capable. To address this trade-off, we propose IRT-Router, a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM. Inspired by Item Response Theory (IRT), a psychological measurement methodology, IRT-Router explicitly models the relationship between LLM capabilities and user query attributes. This not only enables accurate prediction of response performance but also provides interpretable insights, such as LLM abilities and query difficulty. Additionally, we design an online query warm-up technique based on semantic similarity, further enhancing the online generalization capability of IRT-Router. Extensive experiments on 20 LLMs and 12 datasets demonstrate that IRT-Router outperforms most baseline methods in terms of effectiveness and interpretability. Its superior performance in cold-start scenarios further confirms the reliability and practicality of IRT-Router in real-world applications. Code is available at https://github.com/Mercidaiha/IRT-Router.