arXiv:2606.01498v1 Announce Type: new Abstract: Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural language and tools, it remains unclear whether they can conduct reliable time series analysis across multi-turn conversations. Existing benchmarks focus on single-step tasks such as forecasting and anomaly detection, overlooking practical workflows where user goals evolve, agents must build on prior analyses, and conclusions emerge from accumulated evidence. In this work, we introduce TimeSage-MT, a m
The proliferation of large language models and the increasing sophistication of AI agentic systems necessitate more robust evaluation benchmarks that reflect real-world, multi-turn analytical tasks.
Reliable evaluation of AI agents in complex, multi-turn time series analysis is critical for their deployment in high-stakes decision-making across various domains, moving beyond single-step task limitations.
The introduction of TimeSage-MT provides a specific benchmark that shifts the focus of AI agent evaluation from isolated tasks to cumulative, conversational analytic workflows, reflecting practical user interactions.
- · AI agent developers
- · Time series data analytics platforms
- · Businesses adopting AI for complex data analysis
- · Academic AI researchers
- · Single-task AI evaluation methodologies
- · Companies relying on simplistic AI benchmarks
Improved capabilities of AI agents in handling complex, evolving analytical tasks, particularly in time series data.
Accelerated integration of sophisticated AI agents into operational decision-making systems across finance, healthcare, and logistics.
Enhanced trust and broader adoption of AI agents for critical strategic analysis, potentially displacing human analysts in certain multi-turn decision processes.
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Read at arXiv cs.CL