Same model, but evolving how the team is organized brings GPU kernel optimization close to expert-written code 🚀 Evolving Agent Teams 📌 Overview UC Berkeley's EAT co-evolves a coding-agent team's configuration (roles, coordination, knowledge management) together with the solution. ⚠️ The problem ・Agents spend their budget on easy-to-implement directions and get stuck in local optima ・A fixed team can't keep up as the work shifts from debugging to profiling to redesign 🧬 Method ・A supervisor diagnoses failure patterns and designs the next team ・It invents roles from scratch, such as Profiling Scientist and Knowledge Integrator ・A persistent file system carries learnings and artifacts to each new team 📊 Results ・MLA decode (H100): 1.21x over FlashInfer with extended budget, beating it on 36 of 47 workloads ・A fixed four-role team reached 0.31x, and Claude Code plateaued around 0.5x ・GDN prefill (B200) rose from 2.2x to 4.06x, and GEMM+AllReduce reached 1.56x 🔭 Why it matters Without changing the model, adapting the organization itself breaks through plateaus. #AIAgents# #GPUKernels#