Research-Driven Agents: What Happens When Your Agent Reads Before It Codes (blog.skypilot.co) AI

SkyPilot reports a case study where an AI coding agent first performs literature and fork research—rather than starting from code alone—before running many benchmarked experiments. Pointed at llama.cpp CPU inference, the added research phase led to finding several operator fusion and parallelization changes, including softmax and RMS norm fusions and a CPU-specific RMS_NORM+MUL graph fusion. The authors say this produced measurable speedups, with flash-attention text generation up about 15% on x86 and about 5% on ARM for TinyLlama 1.1B, while highlighting that code-only agents may miss key bottlenecks like memory-bandwidth limits.