Google 论文提出 Harness^2：测试时自我改进的智能体框架
Google 出的论文，教你怎么让智能体框架在测试时自己改自己，GLM-5 和 Gemini 3.5 Flash 上都涨分了
Google 发布论文 Harness^2，提出在测试时对智能体框架进行递归自我改进，无需依赖验证器。方法是对当前任务的框架生成多个编辑版本，互相测试后保留效果最好的组合，再将经验存入第二个框架供后续任务使用。实验显示该方法在 GLM-5 和 Gemini 3.5 Flash 上的 WorkBench-Bench、JobBench 和 LAB 测试中全部配置均有提升，并行运行 5 个候选编辑可在 WorkBench-Bench 上再增加 2.9 到 3.9 分。对比的 ReasoningBank 记忆基线在编程任务上反而比基础框架低最多 6.6 分。
Recommended, must-read paper on building recursive, self-improving agent harnesses for open-world tasks. DAIR.AI @dair_ai Learn to build custom harnesses, folks! Here is a highly recommended paper from Google on recursive agent harnessing. Great insights if you work on evolving your harnesses. It also offers an important discussion of recursive self-improvement and evolving harnesses for open-world tasks. They focus on improving your agent harness at test time without a verifier. This allows interesting scaling properties. Harness^2 proposes several edits to the harness for the current task, tests them against each other, and keeps the combination that works best. Lessons from those edits then update a second harness that the agent carries to future tasks. Results: It improved GLM-5 and Gemini 3.5 Flash in every configuration tested across WorkBuddy-Bench, JobBench, and LAB. Running five candidate edits in parallel adds another 2.9 to 3.9 points on WorkBuddy-Bench. ReasoningBank, a memory baseline, fell up to 6.6 points below the base harness on coding tasks in the same comparison. arxiv.org/abs/2610.14224 Chat with Paper: academy.dair.ai/papers/harness… 🔗 View Quoted Tweet 💬 8 🔄 3 ❤️ 16 👀 2026 📊 9 ⚡
