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HarnessBridge replaces the manual engineering bottleneck in LLM agent harness design with an end-to-end trainable module, reducing token usage and trajectory length while maintaining competitive benchmark performance.
The study reveals that a single instruction-tuned model cannot optimally serve both Flow and Command coding modes simultaneously, highlighting a concrete design tension that the authors argue must be carefully balanced in AI-powered coding assistant development.