Survey of Temporal Agent Reasoning Models: A Complete Tech Stack from Data Quality to Multimodal Reasoning

This article surveys the latest advances in the field of temporal agent reasoning models, covering cutting-edge work such as TSQAgent data quality assessment, TSRBench multi-task multimodal benchmark, TimeART tool-augmented reasoning, and process-verifiable reasoning, while organizing the tech stack and application prospects.

Time-series data is widely present in fields such as finance, healthcare, IoT, and industrial monitoring. Traditional analysis relies on statistical models and machine learning techniques. With the development of large language models (LLMs) and agent technologies, temporal analysis has shifted from numerical computation to intelligent reasoning, integrating tool calling and multi-turn dialogue capabilities to understand complex patterns, perform causal inference, and make decisions—driving rapid evolution in the field.

1. TSQAgent: Intelligent agent-driven temporal data quality assessment, featuring multi-dimensional feature extraction (completeness, anomalies, trends, etc.) and dynamic strategy adjustment; 2. TSRBench: A multi-task multimodal benchmark that evaluates four core capabilities—perception, reasoning, prediction, and decision-making—supporting modalities such as text and vision; 3. TimeART: A tool-augmented reasoning architecture, including a tool library (statistical tests, decomposition, etc.), reasoning engine, and memory module; 4. Process-verifiable reasoning: Focuses on the rationality of reasoning steps, constructing thought chain datasets and implementing difficulty-adaptive scheduling.

• TSQAgent constructs a tool-calling dataset, decomposing tasks to invoke specialized tools; - TSRBench was developed by the UMD Zhou Lab and released on Hugging Face, supporting multimodal inputs; - TimeART builds a multi-turn dialogue dataset (including queries, tool-calling chains, intermediate results, etc.); - Process-verifiable reasoning adjusts the difficulty of training samples via dynamic scheduling algorithms to improve model learning outcomes.

The Ts_Agent_reasoning_models repository compiles the latest advances in the field, outlining the blueprint for temporal agent technology. Temporal analysis is shifting from traditional statistical modeling to intelligent reasoning, enhancing accuracy and interpretability, and is expected to become an intelligent assistant in fields like finance, healthcare, and industry in the future.

Technical trends: From single-modal to multimodal, end-to-end to tool-augmented, result-oriented to process-oriented; The open-source ecosystem is thriving, with open sharing across various fields; Application scenarios include financial analysis (stock price trends, risk assessment), healthcare (vital sign analysis), industrial IoT (equipment maintenance), and intelligent customer service (user behavior analysis).