Google Research 发布 Harness² 框架，让智能体在冻结模型上改进自身 harness

Can Agents improve their own harness on unseen tasks? The @GoogleResearch team released Harness², a framework that allows improve an harness around a frozen model.

How it works:
1️⃣ Agent runs the base harness on each task to record a baseline trajectory
2️⃣ Editor agent inspects the trajectory, drafts 2 candidate edits (SI, Skills, hooks,….), and runs both.
3️⃣ Composer agent diffs the new trajectory against the baseline, merges edits that fixed failures (within a cap), and runs the final harness.
4️⃣ Reflector agent logs verified lessons into file and saves the winning components to a library for future tasks to pull on demand.

Results:
- 💡 Gemini 3.5 Flash +8.4 on WorkBuddy-Bench and +7.5 on JobBench.
- 🛠️ Skills and scripts drive ~50% of edits; dropping skills costs 5.3 pts.
- 📚 Gemini 3.5 Flash improves on all 3 benchmarks when editing its own harness.

Paper: https://arxiv.org/abs/2610.14224
Code: https://github.com/google-research/harness2