RoboArchitect 论文：不动模型权重，自动为机器人策略设计 agent harness
RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
一篇机器人方向的论文：不微调模型，让编码模型自己改控制代码，成功率翻了四五倍，还能攒经验，思路挺有意思。
arXiv 论文 RoboArchitect 提出让编码模型观察机器人 rollout、诊断失败原因并迭代改写控制代码（harness），包住冻结的策略模型。不更新任何权重的情况下，在未见场景上的平均成功率从 9.7% 提到 43.3%（4.5 倍），未见任务从 12.7% 提到 40.2%（3.2 倍）。harness 还能自动演化出持久记忆，把有效抓取深度等经验带入后续决策。在策略训练数据覆盖差的基准上，进化出的 harness 表现甚至超过微调后的版本。
RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code decides when to query the policy, consult vision tools, or command the arm directly. Yet today these harnesses are built by hand and redesigned for each new robot or benchmark. We show that harness design can be automated, offering a strong alternative to finetuning on a novel setup. In RoboArchitect, a coding model observes a robot's rollouts, diagnoses failures, and iteratively revises the harness around the frozen policy. The harness evolves on a set of episodes for its optimization and is evaluated on held-out scenes and tasks. Without updating any model weights, RoboArchitect raises average success from 9.7% to 43.3% on unseen scenes (4.5x) and from 12.7% to 40.2% on unseen tasks (3.2x), across multiple benchmarks and policies. The gains are largest where the benchmark is far from the policy's training data: wrapping a general-purpose policy, the evolved harness yields a specialist that often outperforms even the finetuned version. Intuitive control strategies are automatically designed, such as lifting a jammed arm clear or reaching in horizontally when an obstacle blocks the way from above. The harness also evolves its own persistent memory, carrying learned experience, such as effective grasp depths, into later decisions. These results suggest that substantial headroom in robotic manipulation lies in the agent harness, and its design can be effectively automated and optimized without weight updates.