4 papers · 1 filter
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
Shunyu Wu, Dan Li, Haozheng Ye +6
Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs…
Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning
Liuji Chen, Dianxing Tang, Xing Shi +4
Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning. Existing approaches mitigate this issue w…
Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting
Zhuomin Chen, Dan Li, Jiahui Zhou +4
Time series (TS) data are ubiquitous across various application areas, rendering time series forecasting (TSF) a fundamental task. With the astounding advances in large language mo…
Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision
Jiahui Zhou, Dan Li, Lin Li +5
The reasoning capabilities of large language models (LLMs) have significantly advanced their performance by enabling in-depth understanding of diverse tasks. With growing interest…