Jue WANG

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 deployments. Across AIME 2025, HMMT 2025, LiveCodeBench V6, GPQA-Diamond, ARC-AGI-2, multimodal vision benchmarks (MMMU-Pro, BabyVision), and agentic settings (Terminal-Bench~2.0), Squeeze Evolve consistently improves the cost--capability frontier over single-model evolution and achieves new state-of-the-art results on several tasks. Empirically, Squeeze Evolve reduces API cost by up to $\sim$3$\times$ and increases fixed-budget serving throughput by up to $\sim$10$\times$. On circle packing, an open-ended continuous-objective task, Squeeze Evolve is the first verifier-free evolutionary method to exceed AlphaEvolve without executing programs or using closed-weight models.

Sampling in LLM inference comprises a combinatorial set of logit processing, token selection, and verification operations for speculative decoding. However, existing implementations either accelerate only subsets of this pipeline, rely on multiple kernel launches, or assume homogeneous sampling behavior across a batch, limiting support for dynamic serving workloads and preventing efficient CUDA Graph execution. We present **SonicSampler**, a unified suite of tile-aware Triton kernels that vertically fuses the common production sampling pipeline into a fixed, workload-aware execution model. Our kernels support dynamic per-request sampling behaviors, including grammar-constrained decoding, repetition, frequency and presence penalties, logit bias, temperature scaling, top-$k$ / top-$p$ / min-$p$ filtering, and speculative verification, entirely within a fixed set of batched kernels while remaining fully CUDA Graph-compatible. Central to our approach is a novel hierarchical two-stage top-$k$ algorithm that exploits the low-entropy structure of LLM outputs to enable efficient selection over large vocabularies and achieves up to **10x speedup** on 128K vocab sizes over competitive baselines. Across heterogeneous speculative decoding workloads, SonicSampler achieves up to **16x speedup** on draft sampling and up to **10x speedup** on target sampling and verification over state-of-the-art baselines while preserving flexible batched execution.