Table question answering (TQA) requires accurate retrieval and reasoning over tabular data.Existing approaches attempt to retrieve queryrelevant content before leveraging large language models (LLMs) to reason over long tables.However, these methods often fail to accurately retrieve contextually relevant data which results in information loss, and suffer from excessive encoding overhead.In this paper, we propose TALON, a multi-agent framework designed for question answering over long tables.TALON features a planning agent that iteratively invokes a tool agent to access and manipulate tabular data based on intermediate feedback, which progressively collects necessary information for answer generation, while a critic agent ensures accuracy and efficiency in tool usage and planning.In order to comprehensively assess the effectiveness of TALON, we introduce two benchmarks derived from the WikiTableQuestion and BIRD-SQL datasets, which contain tables ranging from 50 to over 10,000 rows.Experiments demonstrate that TALON achieves average accuracy improvements of 7.5% and 12.0% across all language models, establishing a new state-of-the-art in long-table question answering.Our code is publicly available

Although the rise of large language models (LLMs) has introduced new opportunities for time series forecasting, existing LLM-based solutions require excessive training and exhibit limited transferability. In view of these challenges, we propose TimeRAG, a framework that incorporates Retrieval-Augmented Generation (RAG) into time series forecasting LLMs, which constructs a time series knowledge base from historical sequences, retrieves reference sequences from the knowledge base that exhibit similar patterns to the query sequence measured by Dynamic Time Warping (DTW), and combines these reference sequences and the prediction query as a textual prompt to the time series forecasting LLM. Experiments on datasets from various domains show that the integration of RAG improved the prediction accuracy of the original model by 2.97% on average.