AI 中文总结
Apodex 1.1 从环境扩展与智能体协调扩展两维度开发工作能力，在多领域复杂任务中性能领先，其 350亿参数的 Mini 版本可本地部署，助力构建长周期任务的重型求解器。
AI 中文摘要
通用语言模型具备推理与综合知识的能力，但复杂工作还需要与文件、信息源、可执行代码进行持续交互，同时需维护状态、实现故障恢复、提供可验证的交付成果，我们将这一能力称为“工作能力”：即朝着现实世界目标持续、可验证地推进。Apodex 1.1 从两个互补维度开发该能力：“环境扩展”扩展了可执行文件、搜索及代码环境的多样性与可验证性；“智能体协调扩展”则训练智能体分解长周期任务、委派并行工作、整合异步结果并重新规划。共享执行工具与 AgentOS 可在工具和智能体间维护任务状态与溯源信息，训练过程将环境轨迹与协调轨迹转化为可靠行为。在复杂专业工作、金融、科研、数学、编码及搜索等场景中，Apodex 1.1 虽使用比许多前沿系统小得多的模型，仍达到领先性能水平；350亿参数的 Apodex 1.1 Mini 更以可本地部署的形式保留了强大的工作能力。这些成果将智能体智能建立在随时间完成的有用、可验证工作的基础上，推进了我们构建用于雄心勃勃的长周期任务的“重型求解器”的目标。
英文摘要
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.
