JIT-Agent: Weaker Models Beat Stronger Ones via Self-Written Scaffolding
krishnan · x · 2026-08-30
The JIT-Agent paper introduces a paradigm shift where a dedicated model dynamically writes and optimizes agent scaffolding—such as memory management, planning strategies, and tool orchestration—instead of relying on manual construction.
Key Mechanisms:
- The model synthesizes scaffolding on-the-fly for specific tasks and repairs it when execution becomes unstable.
- The system improves by distilling signals from a growing archive of prior configurations.
Results:
- DeepSeek-V4-Flash, equipped with a JIT-Agent harness, outperformed GPT-5.6 by 9.1 points on DeepSearchQA and 4.3 on OdysseyBench.
- GLM-5.2 saw gains of up to 20.2 points.
- Generated harnesses proved competitive with mature, expert-designed ones.
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