The Avengers: A Routing Recipe for Collective Intelligence in Language Models - Zhang, Yiqun; Li, Hao; Wang, Chenxu; Chen, Linyao; Zhang, Qiaosheng; Ye, Peng; Feng, Shi; Wang, Xinrun; Xu, Jia; Bai, Lei; Hu, Shuyue (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Proprietary models are increasingly dominating the race for ever-larger language models. Can open-source, smaller models remain competitive across a broad range of tasks? In this paper, we present the Avengers---a lightweight framework that leverages the collective intelligence of these smaller models. The Avengers builds upon four lightweight operations: (i) embedding: encode queries using a text embedding model;(ii) clustering: group queries based on their semantic similarity;(iii) scoring: scores each model's performance within each cluster;and (iv) voting: improve outputs via repeated sampling and voting.At inference time, each query is embedded and assigned to its nearest cluster. The top-performing model(s) within that cluster are selected to generate the response with repeated sampling.Remarkably, with 10 open-source models (~7B parameters each), the Avengers surpasses GPT-4o, 4.1, and 4.5 in average performance across 15 diverse datasets spanning mathematics, coding, logical reasoning, general knowledge, and affective tasks. In particular, it surpasses GPT-4.1 on mathematics tasks by 18.21% and on code tasks by 7.46%. Furthermore, the Avengers delivers superior out-of-distribution generalization, and remains robust across various embedding models, clustering algorithms, ensemble strategies, data efficiency, and values of its sole parameter---the number of clusters.