Google open-sources Harness² research on recursive agent harness improvement
谷歌开源RSI新成果！华人浓度爆表
- Who
- Google researchers
- What
- introduced Harness², a recursive framework for improving agent harnesses
- Quote
谷歌团队这次提出来的Harness²，就是专门冲着这两个死穴开刀的。
What happened
Google researchers introduced and open-sourced Harness², a system that modifies agent harness code, system prompts, tools, and skills during execution. It combines task-level proposal, probing, and composition with a persistent library of improvement experience, supporting OpenCode and Codex. The reported WorkBuddy-Bench results show GLM-5 improving from 64.2 to 75.7 with OpenCode after a single improvement step, exceeding the unmodified Gemini 3.5 Flash score of 72.4.
Why it matters
The reported comparison shows how harness changes can alter model rankings on the same benchmark, making execution setup a consequential variable when assessing agent performance.
The story so far
Google researchers introduced and open-sourced Harness², a system that modifies agent harness code, system prompts, tools, and skills during execution; reported WorkBuddy-Bench results show GLM-5 with OpenCode improving from 64.2 to 75.7 after a single improvement step. Harness² supports OpenCode and Codex. It proposes, probes, and combines changes for individual tasks, while maintaining a persistent library of experience from improvements. In the reported benchmark comparison, the improved GLM-5 setup exceeded the unmodified Gemini 3.5 Flash score of 72.4.
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