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Tsinghua and Z.AI open-source HarnessPro: agents rewrite their own harness, reaching 80.8% on Terminal-Bench 2 at 34% lower cost per task
HarnessPro (arXiv 2610.26770, submitted Oct 19 and revised Oct 20), from Tsinghua University, Z.AI (Zhipu) and collaborators, is a label-free way to improve an agent harness: specialized agents diagnose execution traces, rewrite instructions and runtime policies, score candidates with LLM-generated tests, and keep Lite, Medium and Heavy harnesses along the performance-cost frontier. Per the paper, with GPT-5.5 and Codex the Heavy mode reaches 80.8% on Terminal-Bench 2, 4.5 points above the prior RHO method, while cost per task drops from $1.09 to $0.72. The code is MIT-licensed and drew wider media coverage this week, including from Pandaily.
Key Takeaways
- Terminal-Bench 2 (GPT-5.5 with Codex, mean of 3 runs): Heavy reaches 80.8% at $0.72 per task, versus RHO at 76.3% and $1.09, a 33.9% cost cut (paper figures, arXiv)
- Averaged over three benchmarks: +2.0 points pass rate and -26.1% cost per task versus RHO, across Terminal-Bench 2, SWE-Bench Pro and ProgramBench
- Holds across models: with GLM-5.2 and Claude Code on Terminal-Bench 2, Heavy hits 81.4% at $0.62, versus vanilla at 74.6% and $0.92 and RHO at 78.0% and $1.00
- Budget modes: Medium matches RHO at 76.3% on Terminal-Bench 2 for only $0.56 per task; Lite gets 72.9% at $0.43
- Cheap to train: only 10 training tasks per benchmark and 3 to 4 evolution epochs, tested on 59, 100 and 100 held-out tasks; the three harness tiers are MIT-licensed on GitHub
Linked Models & Agents:
