AION: Next-Generation Tasks and Practical Harness for Time Series
For time series researchers and practitioners, this work proposes a paradigm shift from fixed benchmarks to realistic tasks with temporal constraints and reliability mechanisms, though the empirical evidence is limited to a single case study.
The paper formalizes next-generation time series tasks as three-component tuples and introduces AION, a harness for realistic time series tasks. In a Kaggle Store Sales case study, AION produced more detailed process traces, more artifacts, and more review steps than the same base agent in direct build mode.
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.