JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution

Key points

Viewing agent capability as the combined result of the model and harness, it proposed a JIT approach that generates a harness on the fly for each task instead of fixing one in advance.
It represents the harness as executable code divided into four modules, memory, planning, action, and capability, enabling generation, repair, and evolution.
It trained the generator by combining task-specific learning, failure-recovery learning, and Evo-GDPO, which accumulates better-performing harnesses.
DeepSeek-V4-Flash surpassed GPT-5.6 by 9.1 and 4.3 points on two benchmarks, while GLM-5.2 also improved by up to 20.2 points depending on the task.
Changing the entire execution framework on the fly leaves stability and verification issues in operational environments, and the authors themselves identified a stable core with verifiable partial modifications as future work.

External research summaries. These are not HDATF publications or measured product results.