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该论文提出了一个统一的“工作智能体”（work agents）框架，将基础模型与运行时框架结合以可靠执行复杂数字任务，引入持久性与开放性两个维度，并系统分析能力族、模型与框架优化及评估方法。
Introduces EvoHarness-RL, a framework that learns runtime harness policies for long-horizon LLM agents, enabling them to construct and update external state (belief, progress, experience) during task execution. Using Qwen3-8B on ALFWorld, it achieves 96.9% success and reveals harness annealing and evolution dynamics.
Harness-R1 introduces a method that learns to edit executable runtime harnesses for LLM agents from failure trajectories, using a separate 9B engineer optimized with online GRPO while keeping the target model frozen. It improves success rates across WebShop, ALFWorld, and DBBench.
Proposes Life-Harness, a method that improves frozen LLM agents by adapting the runtime interface instead of model weights, achieving an average 88.5% relative improvement across 126 settings and 18 backbones.