构建面向真实场景的时间序列任务框架，提升模型推理可靠性。
AION: Next-Generation Tasks and Practical Harness for Time Series
- 提出三元组任务结构：任务文件+工作区+验证接口
- 实测生成更多过程痕迹与审查步骤，提升决策可信度
- 适合需要可解释性与多步推理的工业级时序分析场景
时间序列研究正从固定预测基准转向融合预测、上下文推理、工具使用和结构化决策支持的现实任务。现有基准多基于干净数据和短周期评估，单一代理可能忽略时间约束、证据核查或最终输出前的审查。我们首次将下一代时间序列任务形式化为三元组结构：任务文件、工作区与验证接口。进而提出AION框架，由六类组件构成：代理、技能、规则、记忆、评估与协议。设计遵循三大原则：时间锚定、时间知识驱动推理、可靠性机制（如实验后分析与分层审查）。在Kaggle门店销售案例中，该框架相比基线代理在OpenCode直接构建模式下，产生更详细的过程追踪、更多产出物与更多审查步骤。结果表明，应从固定任务向受现实约束的真实任务范式转变。
原文摘要 · Abstract (English)
Time series research is moving beyond fixed forecasting benchmarks toward realistic tasks that combine prediction, contextual reasoning, tool use, and structured decision support. Most benchmarks are built around clean data and short evaluation loops; agents alone may miss temporal constraints, evidence checks, or review before finalizing outputs. We first formalize next-generation time series tasks as three-component tuples consisting of a task file, a workspace, and a validation interface. We then present AION, a time series harness built from six component groups: agents, skills, rules, memory, evaluation, and protocols. In this harness, we use three design principles: temporal grounding, temporal knowledge-grounded reasoning, and reliability mechanisms such as post-experiment analysis and layered review. A Kaggle Store Sales case study shows that the harness produces more detailed process traces, more artifacts, and more review steps than the same base agent operating in OpenCode direct build mode. Taken together, these results argue for a paradigm shift from fixed tasks to realistic ones under real-world constraints.
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