AI RESEARCH
ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants
arXiv CS.AI
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ArXi:2604.18616v1 Announce Type: cross LLM-based coding agents can generate functionally correct GPU kernels, yet their performance remains far below hand-optimized libraries on critical computations such as matrix multiplication, attention, and Mixture-of-Experts (MoE). Peak GPU performance requires coordinated reasoning over tightly coupled optimizations, including tiling, shared-memory staging, software pipelining, and instruction scheduling, while existing agents rely on sparse pass/fail feedback, leaving them unable to diagnose global constraint violations.