First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI
Socher’s automated-research design combines prior knowledge, measurement data, simulation, physical experiments, and agent orchestration, with early demonstrations in training and CUDA optimization.
Richard Socher proposes a **four-pillar** research system: existing knowledge, scientific measurement data, simulations, and physical labs, coordinated by an agent swarm. Recursive AI reports early experiments improving small-model training, training speed, and Nvidia CUDA kernels.
Builders of research agents should treat discovery as an ideation, implementation, and validation loop with rewards grounded in simulations or experiments. Keep recursive self-improvement distinct from an agent merely optimizing a separate model or benchmark.
Richard Socher proposes a **four-pillar** research system: existing knowledge, scientific measurement data, simulations, and physical labs, coordinated by an agent swarm. Recursive AI reports early experiments improving small-model training, training speed, and Nvidia CUDA kernels. Builders of research agents should treat discovery as an ideation, implementation, and validation loop with rewards grounded in simulations or experiments. Keep recursive self-improvement distinct from an agent merely optimizing a separate model or benchmark. The talk provides high-level proof points rather than full protocols or quantitative results. Claims of beating teams and benchmark leaders were reportedly checked for reward hacking, but the supplied material is insufficient to assess reproducibility, cost, or transfer beyond the tested tasks.
This extends agent engineering from completing predefined work to proposing and validating research improvements across knowledge, simulation, measurement, and labs. Against prior candidates, it makes experimental rewards and reproducibility the decisive verification layer: introspection or judge scores alone cannot establish discovery, while swarm structure introduces coordination and latent-objective risks that the high-level results do not resolve.