JIT-Agent paper: task-specific harness lifts smaller model to 85.1 vs GPT-5.6's 76.0
GlenBradley · x · 2026-09-02
A new paper introduces JIT-Agent, which generates the agent harness on demand for each specific task — choosing how memory, planning, actions, tools, and skills should be organized, rather than reusing a static harness built for one purpose.
- Key claim: a smaller model with the right task-specific harness can beat a stronger model
- Numbers: DeepSeek-V4-Flash + JIT-Agent scored 85.1 on DeepSearchQA, versus 76.0 for GPT-5.6
- Commenters see it as a profound shift: turning the harness from a hand-engineered artifact into a dynamically generated one
Related event: JIT-Agent Paper Lets Small Models Outperform GPT-5.6 at Lower Cost(2 posts)→
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