One Head, Many Models: Cross-Attention Routing for Cost-Aware LLM Selection

• URL: http://arxiv.org/abs/2509.09782v1
• Date: Thu, 11 Sep 2025 18:29:09 GMT
• Title: One Head, Many Models: Cross-Attention Routing for Cost-Aware LLM Selection
• Authors: Roshini Pulishetty, Mani Kishan Ghantasala, Keerthy Kaushik Dasoju, Niti Mangwani, Vishal Garimella, Aditya Mate, Somya Chatterjee, Yue Kang, Ehi Nosakhare, Sadid Hasan, Soundar Srinivasan,
• Abstract summary: Large language models (LLMs) with varying computational costs and performance profiles present a critical challenge for scalable, cost-effective deployment in real-world applications.<n>We introduce a unified routing framework that leverages a single-head cross-attention mechanism to jointly model query and model embeddings.<n>By explicitly capturing fine-grained query-model interactions, our router predicts both response quality and generation cost, achieving up to 6.6% improvement in Average Improvement in Quality (AIQ) and 2.9% in maximum performance over existing routers.
• Score: 3.872690949369412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of large language models (LLMs) with varying computational costs and performance profiles presents a critical challenge for scalable, cost-effective deployment in real-world applications. We introduce a unified routing framework that leverages a single-head cross-attention mechanism to jointly model query and model embeddings, enabling dynamic selection of the optimal LLM for each input query. Our approach is evaluated on RouterBench, a large-scale, publicly available benchmark encompassing diverse LLM pools and domains. By explicitly capturing fine-grained query-model interactions, our router predicts both response quality and generation cost, achieving up to 6.6% improvement in Average Improvement in Quality (AIQ) and 2.9% in maximum performance over existing routers. To robustly balance performance and cost, we propose an exponential reward function that enhances stability across user preferences. The resulting architecture is lightweight, generalizes effectively across domains, and demonstrates improved efficiency compared to prior methods, establishing a new standard for cost-aware LLM routing.

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