Harnessing Generalist Agents for Contextualized Time Series

2026-06-03

AI Summary

This work addresses the lack of native support for structured time series in general-purpose AI agents, which hinders end-to-end temporal reasoning within rich contextual environments. To overcome this limitation, the authors propose TimeClaw, a novel framework that integrates executable time-series tools, an experience-driven subroutine evolution mechanism, and multimodal episodic memory into large language model agents, thereby endowing them with native temporal reasoning capabilities. TimeClaw enables traceable, reusable, and open-ended time-series analysis workflows. Extensive evaluations across multiple domains—including energy, finance, meteorology, and transportation—demonstrate that TimeClaw significantly outperforms existing approaches, validating its effectiveness and generality in real-world scenarios.

Abstract

Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end workflows for analyzing temporal dynamics, where widely studied tasks such as forecasting are only one step in a broader solution loop. While generalist AI agents offer a promising interface for such workflows under complex contexts, they still operate primarily in textual spaces that are not fully aligned with structured temporal signals. In this work, we introduce TimeClaw, an agentic harness framework for time series that equips generalist LLM agents with the time series-native runtime support needed for contextualized temporal reasoning. TimeClaw integrates executable temporal tools for grounded and auditable analysis, experience-driven capability evolution for creating reusable analytical routines, and episodic multimodal memory for retrieving relevant reasoning traces. Together, these components unlock harnessed open-ended temporal reasoning with contextual information. Extensive evaluation on multiple benchmarks covering diverse tasks across energy, finance, weather, traffic, and other real-world domains demonstrates improved performance of TimeClaw. Code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/TimeClaw.

Problem

• time series
• generalist AI agents
• contextualized reasoning
• temporal dynamics
• structured temporal signals

Innovation

• time series reasoning
• generalist AI agents
• executable temporal tools
• experience-driven evolution
• multimodal memory