ECLIPSE: An Executable Contextual Intervention and Planning Agent for Time-Series Tasks

Real-world time series cannot be fully characterized by historical observations alone, because their dynamics also depend on sparse contextual clues, external events, and domain knowledge. Existing methods often flatten such context into a prompt and require a single model pass to perform both semantic grounding and numerical extrapolation, leading to brittle reasoning. We propose ECLIPSE, an agentic system that recasts context-rich time-series reasoning as executable scenario reconstruction. ECLIPSE assembles a task-relevant scenario state from textual clues, retrieved temporal precedents, and domain priors, then compiles it into a temporal program. A unified planner selects contextual constraints and temporal operations, while foundation models and executable operators perform numerical computation. An audit agent checks contextual and numerical consistency, triggering replanning or a fallback to the unedited forecast when necessary. Across context-rich forecasting and temporal QA benchmarks, ECLIPSE outperforms direct prompting with the same language model, and a controlled comparison under a shared toolchain attributes the gain to the orchestration of history-side and future-side interventions rather than to tool access alone.