Impressive paper from Stanford on harness evolution for robotics.

They improve frozen robot policies from 9.7% to 43.3% success on unseen scenes without changing a single weight.

The gain comes from the harness.

RoboArchitect gives a coding model the robot's rollouts. The coding model diagnoses failures and rewrites the control code around the frozen policy. That code decides when to call the policy, when to use vision tools and when to command the arm directly.

The harness is optimized on one set of episodes and tested on held-out scenes and tasks. Success on unseen tasks rises from 12.7% to 40.2%.

The largest gains appear where the benchmark is far from the policy's training data. Inside an evolved harness, a general-purpose policy often beats the finetuned version of the same policy.

The search also produced control routines such as lifting a jammed arm clear, and it added a persistent memory that stores values like effective grasp depths.

Paper: https://arxiv.org/abs/2610.42141

Chat with Paper: https://academy.dair.ai/papers/roboarchitect-automating-the-design-of-robotic-agents-with-persistent-memory-2610.42141