Recursive language models (RLMs) showed that recursion over model calls is an effective strategy for long-context reasoning, and production coding agents have begun to write code that spawns subagents at scale, most recently in Anthropic's dynamic workflows. We name and study the pattern between these two lines of work, where the recursive unit is a full agent harness with filesystem tools, code execution, and planning rather than a model call with no tools. We call this the Recursive Agent Harness (RAH) and frame it as harness recursion, the code-first extension to the model recursion of RLMs. A parent agent generates and runs an executable script that spawns subagent harnesses in parallel for fine-grained workloads and uses structured function calls for small subtasks. We provide a controlled evaluation on long-context reasoning. With the backbone held fixed at GPT-5 to match the published Codex and RLM baselines, RAH improves the Codex coding-agent baseline from 71.75% to 81.36% on Oolong-Synthetic (199 samples, 13 context-length buckets up to 4M tokens), a gain attributable to the harness rather than the model. With a stronger backbone, Claude Sonnet 4.5, the same design reaches 89.77%.

翻译：递归语言模型表明，在模型调用上进行递归是长上下文推理的有效策略，而生产级编程代理已开始编写可大规模生成子代理的代码，最显著的是Anthropic的动态工作流。我们将这两类工作之间的模式进行命名并研究，其中递归单元是一个完整的代理框架，包含文件系统工具、代码执行和规划，而非无工具的模型调用。我们将此称为递归代理框架（RAH），并将其定义为框架递归——这是对RLM模型递归的代码优先扩展。父代理生成并运行一个可执行脚本，该脚本为细粒度工作负载并行生成子代理框架，并使用结构化函数调用处理小型子任务。我们提供了长上下文推理的受控评估。在固定主干网络为GPT-5以匹配已发表的Codex和RLM基线时，RAH在Oolong-Synthetic数据集上（199个样本，13个上下文长度区间，最高达400万token）将Codex编程代理基线从71.75%提升至81.36%，这一提升归因于框架而非模型。当使用更强的骨干网络Claude Sonnet 4.5时，相同设计达到89.77%。