[2604.07725] Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

Computer Science > Artificial Intelligence arXiv:2604.07725 (cs) [Submitted on 9 Apr 2026 (v1), last revised 10 Apr 2026 (this version, v2)] Title:Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution Authors:Monishwaran Maheswaran, Leon Lakhani, Zhongzhu Zhou, Shijia Yang, Junxiong Wang, Coleman Hooper, Yuezhou Hu, Rishabh Tiwari, Jue Wang, Harman Singh, Qingyang Wu, Yuqing Jian, Ce Zhang, Kurt Keutzer, Tri Dao, Xiaoxia Wu, Ben Athiwaratkun, James Zou, Chenfeng Xu View a PDF of the paper titled Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution, by Monishwaran Maheswaran and 18 other authors View PDF HTML (experimental) Abstract:We show that verifier-free evolution is bottlenecked by both diversity and efficiency: without external correction, repeated evolution accelerates collapse toward narrow modes, while the uniform use of a high-cost model wastes compute and quickly becomes economically impractical. We introduce Squeeze Evolve, a unified multi-model orchestration framework for verifier-free evolutionary inference. Our approach is guided by a simple principle: allocate model capability where it has the highest marginal utility. Stronger models are reserved for high-impact stages, while cheaper models handle the other stages at much lower costs. This principle addresses diversity and cost-efficiency jointly while remaining lightweight. Squeeze Evolve naturally supports open-source, closed-source, and mixed-model deploy...