JIT-Agent：通过即时框架演化实现框架智能的规模扩展

摘要

智能体的能力并非仅由模型本身决定。智能体框架（agent harness）涵盖记忆管理、规划策略、动作协议以及工具/技能编排，其对整体能力的贡献可能远超底层基础模型。然而，框架设计仍依赖人工、针对特定任务，从根本上缺乏可扩展性。我们提出了JIT-Agent，一个框架智能模型，经过训练可为任意开箱即用的智能体大语言模型即时合成任务自适应的智能体框架。我们将智能体框架形式化为一个可组合、机器可生成的构件，由固定的四模块协议约束，并训练JIT-Agent针对手头任务定制框架、修复框架以确保稳定可靠的执行，以及通过从不断扩展的历史框架配置库中蒸馏性能信号来实现自我进化。配备JIT-Agent作为框架助手后，DeepSeek-V4-Flash在DeepSearchQA（+9.1）和OdysseyBench（+4.3）上超越了GPT-5.6，而本就强大的GLM-5.2也获得了最高+20.2分的提升。在受控评估中，JIT-Agent生成的框架在性能上与OpenCode和Claude Code等成熟的智能体运行时不相上下，并能持续提升DeepSeek V4、Mimo-V2.5和Qwen3.6等多尺度模型系列的表现。据我们所知，JIT-Agent是首个专为即时框架生成而构建的模型，确立了框架智能作为智能体能力的一个可训练、可迁移且可叠加的维度，与模型扩展相互正交。

English

Agent capability is not determined by the model alone. The agent harness, encompassing memory management, planning strategy, action protocol, and tool/skill orchestration, can dominate the contribution of the underlying foundation model. Yet harness design remains manual, task-specific, and fundamentally unscalable. We present JIT-Agent, a harness intelligence model trained to synthesize task-adaptive agent harnesses on the fly for arbitrary off-the-shelf agentic LLMs. We formalize the agent harness as a composable, machine-generatable artifact governed by a fixed four-module protocol, and train JIT-Agent to customize harnesses for a given task at hand, repair harnesses for stable and reliable execution, and self-evolve by distilling performance signals from an expanding archive of prior harness configurations. Equipped with JIT-Agent as a harness helper, DeepSeek-V4-Flash surpasses GPT-5.6 on DeepSearchQA (+9.1) and OdysseyBench (+4.3), while the already strong GLM-5.2 gains up to +20.2 points. Across controlled evaluations, JIT-Agent-generated harnesses are performance-competitive with mature agent runtimes such as OpenCode and Claude Code and consistently improve multi-scale model families of DeepSeek V4, Mimo-V2.5, and Qwen3.6. To our knowledge, JIT-Agent is the first model purpose-built for just-in-time harness generation, establishing harness intelligence as a trainable, transferable, and compounding dimension of agent capability orthogonal to model scaling.