TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering

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 add