让大模型能持续完成复杂任务，支持文件、代码和搜索的可靠协作。
Apodex 1.1: Scaling Agentic Intelligence for Complex Work
- 通过环境扩展与智能体协作，实现长期任务的分解与执行。
- 350亿参数模型在多领域表现领先，本地部署仍具强工作能力。
- 适合需要持续推理与跨工具协同的科研、编程等复杂场景。
通用语言模型虽具推理与知识整合能力，但复杂工作还需持续交互文件、信息源与可执行代码，并保持状态、处理失败、确保成果可验证。我们称之为「工作能力」：向真实目标持续推进并可验证的过程。Apodex 1.1 在两个互补维度上发展此能力：环境扩展（Environment Scaling）提升文件、搜索与代码环境的多样性与可验证性；智能体协作扩展（Agentic Coordination Scaling）训练智能体拆解长周期任务、并行委派、异步集成结果并重规划。共享执行框架与AgentOS维护跨工具与智能体的任务状态与溯源记录，训练将环境轨迹与协调路径转化为可靠行为。在金融、科研、数学、编程与搜索等复杂专业任务中，Apodex 1.1 达到领先性能，且所用模型远小于多数前沿系统。350亿参数的Apodex 1.1 Mini版本仍具备强大工作能力，支持本地部署。这些成果将智能体智能扎根于时间跨度内的可验证工作中，推动构建面向宏大、长期任务的「重型求解器」。
原文摘要 · Abstract (English)
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this \emph{working capability}: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. \emph{Environment Scaling} expands the diversity and verifiability of executable file, search, and code environments, while \emph{Agentic Coordination Scaling} trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan. A shared execution harness and AgentOS maintain task state and provenance across tools and agents, and training turns environment trajectories and coordination traces into reliable behavior. Across complex professional work, finance, scientific research, mathematics, coding, and search, Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems. The 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form. These results ground agentic intelligence in useful, verifiable work completed over time and advance our goal of building a \emph{Heavy-Duty Solver} for ambitious, long-running tasks.
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