HarnessX: 一个可组合、自适应且可演化的智能体框架铸造厂
HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
AI总结：
提出HarnessX，通过替换代数组合框架原语、基于AEGIS多智能体演化引擎自适应调整，并利用轨迹反馈闭环优化，在五个基准上平均提升14.5%性能。
摘要：
AI智能体的性能关键依赖于运行时框架，包括提示、工具、记忆和控制流，这些中介了模型如何观察、推理和行动。然而，当今的框架在很大程度上仍然是手工制作和静态的：每个新模型或任务仍然需要定制的脚手架，并且在执行过程中产生的丰富轨迹很少被提炼为系统性的改进。我们引入了HarnessX，一个用于可组合、自适应和可演化的智能体框架的铸造厂。HarnessX通过替换代数组装类型化的框架原语，通过AEGIS（一个基于轨迹驱动的多智能体演化引擎，建立在符号适应与强化学习之间的操作镜像上）进行自适应，并通过将轨迹转化为框架更新和模型训练信号来闭合框架-模型循环。在五个基准测试（ALFWorld、GAIA、WebShop、tau^3-Bench和SWE-bench Verified）上，HarnessX平均提升了14.5%（最高达44.0%），其中基线最低时增益最大。这些结果表明，智能体的进步不一定来自模型规模的扩展：从执行反馈中组合和演化运行时接口是一个可行且互补的杠杆。完整的代码库将在未来版本中开源。
英文摘要：
AI agent performance depends critically on the runtime harness, comprising the prompts, tools, memory, and control flow that mediate how a model observes, reasons, and acts. Yet today's harnesses remain largely hand-crafted and static: each new model or task still demands bespoke scaffolding, and the rich traces produced during execution are rarely distilled back into systematic improvement. We introduce HarnessX, a foundry for composable, adaptive, and evolvable agent harnesses. HarnessX assembles typed harness primitives via a substitution algebra, adapts them through AEGIS, a trace-driven multi-agent evolution engine grounded in an operational mirror between symbolic adaptation and reinforcement learning, and closes the harness-model loop by turning trajectories into both harness updates and model training signal. Across five benchmarks (ALFWorld, GAIA, WebShop, tau^3-Bench, and SWE-bench Verified), HarnessX yields an average gain of +14.5% (up to +44.0%), with gains largest where baselines are lowest. These results suggest that agent progress need not come from model scaling alone: composing and evolving runtime interfaces from execution feedback is an actionable and complementary lever. Project homepage: https://darwin-agent.github.io/HarnessX/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
