New benchmark tests LLM agents on multi-turn time series reasoning

Researchers have introduced TimeSage-MT, a new benchmark designed to evaluate the time series reasoning capabilities of large language model agents across multi-turn conversations. The benchmark includes 240 tasks and over 2,600 dialogue turns, covering real-world domains and focusing on evolving user goals and accumulated evidence. Initial evaluations using TimeSage-MT revealed significant performance drops in decision-oriented tasks, highlighting critical gaps in agent memory, uncertainty handling, and domain-specific decision-making. AI

IMPACT This benchmark will drive development of more capable LLM agents for complex, multi-turn data analysis tasks.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

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• LLM agents
• TimeSage
• TimeSage-MT

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