谷歌开源Harness²：智能体可自我改进外围脚手架
AI 总结
谷歌研究团队在 GitHub 以 Apache 2.0 协议开源了 Harness²（Recursive Agent Harnessing for an Open World）。该框架不改模型权重，而是通过任务级的“提议—探测—合成”递归和跨任务的经验库，持续修改系统提示词、工具、记忆与子智能体等外围组件，并支持 OpenCode 与 Codex。团队称在 WorkBuddy-Bench、JobBench 和 LAB 上使用 Gemini 3.5 Flash 与 GLM-5 测试，单步优化即可提升成绩，例如 GLM-5 在 WorkBuddy-Bench 的 OpenCode 配置下从 64.2 升至 75.7；谷歌同时说明这仍是研究项目，并非官方支持产品，且递归会增加测试时算力消耗。
深度见解
若这些厂商自测结果成立，Harness² 展示了一条不靠改权重、而靠运行时脚手架自我修改来提升智能体表现的路径；不过其递归搜索需要更多模型调用，且官方提醒这仍是研究项目。
| Google just released Harness2 as open source. It turns any AI harness into a system that improves itself over time. A harness is everything around the model. System prompt, tools, memory, skills, and subagents. Normally a person designs it once and the agent uses that fixed setup for every new task. With Harness2 the agent itself refines the harness for each task. It proposes changes, runs a few candidate versions, and picks a better one by comparing how they perform. No correct answers or graders are needed. The model weights stay the same. What it learns on one task carries over to the next. After finishing a task it saves the improved harness and useful lessons so future edits start smarter instead of from scratch. Google tested it with Gemini 3.5 Flash and GLM-5 on legal, office, and professional work benchmarks. Even one round of edits beat the original harnesses. More rounds pushed scores higher. GitHub https://github.com/google-research/harness2 submitted by /u/chemist_slime [link] [comments] |
#Google Research#Harness²#Gemini 3.5 Flash#GLM-5#OpenCode#Codex#WorkBuddy-Bench#JobBench#LAB#Jev 1.14