LLM-agent-written programs reportedly outperformed expert-built robot motion-planning systems

A blogger describing a Princeton group’s paper says LLM-based agents wrote robot motion-planning programs with significantly higher success rates and much faster performance than expert-built systems based on a general solver. After examining 112 agent-written programs, the blogger says he found no new algorithms: the agents adapted established methods to specific tasks and measured missing information themselves.