The authors introduce Growing Harness, a training paradigm in which an initially strategy-free scaffold exposes fixed model and tool interfaces but contains no predefined task-solving controller.
Agents Learn Reusable Control Code Instead of Rebuilding Context
A failure-guided system converts recurring agent decisions into persistent harness code, reducing model calls while preserving task success.
Research Lab
Laizhen Li · Jiarui Li · Juanjuan Zhao · Kejiang Ye · Ye Li · Cheng-zhong Xu · +1 more
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Shenzhen University of Advanced Technology · University of Macau
Research Digest··2 min read
Thread:Agent Harness Optimization
Li et al.
Why this paper
From Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences and 3 others · Part of Agent Harness Optimization, now 90 papers
In one line
Growing Harness learns reusable agent control code from task failures, reducing LLM calls by up to 91.8% while maintaining or improving task success.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks
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