Google 论文 Harness^2 提出递归式 agent harness 优化方法
DAIR.AI 推荐 Google 关于 recursive agent harnessing 的论文 Harness^2（arxiv.org/abs/2610.14224）。
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/harne…
来源：dair_ai · x.com