评分 6.4 · 来源：cs.AI updates on arXiv.org · 发布于 2026-04-08
评分依据：有一定参考价值的AI研究论文
arXiv:2510.07432v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong symbolic and compositional reasoning, yet they struggle with time series question answering as the data is typically transformed into an LLM-compatible modality, e.g., serialized text, plotted images, or compressed time series embeddings. Such conversions impose representation bottlenecks, often require cross-modal alignment or finetuning, and can exacerbate hallucination and knowledge leakage. To address these limitations, we propose TS-Agent, an agentic, tool-grounded framework that uses LLMs stri