Research-Driven Agents: What Happens When Your Agent Reads Before It Codes
5 hours ago
- #Code Optimization
- #Performance Engineering
- #AI Coding Agents
- Coding agents perform better optimizations when they include a literature research phase, studying papers and competing projects, before writing code.
- Adding research to the autoresearch loop enabled the agent to produce 5 optimizations for llama.cpp, making flash attention text generation 15% faster on x86 and 5% faster on ARM.
- The agent's research highlighted that studying forks (like ik_llama.cpp) and other backends (CUDA/Metal) was more productive than searching arXiv alone.
- Five successful optimizations included kernel fusions (softmax, RMS norm, flash attention KQ) and adaptive parallelization, reducing memory passes and improving performance.
- The setup is cost-effective (~$29 over 3 hours) and works with any project that has a benchmark and test suite, using cloud VMs via SkyPilot.