TS-Agent: A Time Series Reasoning Agent with Iterative Statistical Insight Gathering

2025-10-08

AI Summary

LLMs suffer from hallucination and knowledge leakage in time-series reasoning, undermining reliability. To address this, we propose a decoupled reasoning agent architecture that strictly separates the LLM from time-series analysis: the LLM handles high-level logical reasoning and evidence integration, while atomic operators—directly processing raw numerical sequences—invoke domain-specific statistical tools to extract structured features. We further introduce explicit evidence logging, self-feedback loops, and quality gating to enable interpretable, verifiable, and iterative reasoning. This design effectively suppresses hallucination by grounding all inference steps in empirical data. In zero-shot evaluation across multiple benchmarks, our method outperforms state-of-the-art models in reasoning accuracy, matches SOTA performance in comprehension tasks, and demonstrates significantly improved generalization and robustness.

Technology Category

• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) have shown strong abilities in reasoning and problem solving, but recent studies reveal that they still struggle with time series reasoning tasks, where outputs are often affected by hallucination or knowledge leakage. In this work we propose TS-Agent, a time series reasoning agent that leverages LLMs strictly for what they excel at, i.e., gathering evidence and synthesizing it into conclusions through step-by-step reasoning, while delegating the extraction of statistical and structural information to time series analytical tools. Instead of mapping time series into text tokens, images, or embeddings, our agent interacts with raw numeric sequences through atomic operators, records outputs in an explicit evidence log, and iteratively refines its reasoning under the guidance of a self-critic and a final quality gate. This design avoids multi-modal alignment training, preserves the native form of time series, ensures interpretability and verifiability, and mitigates knowledge leakage or hallucination. Empirically, we evaluate the agent on established benchmarks. Our experiments show that TS-Agent achieves performance comparable to state-of-the-art LLMs on understanding benchmarks, and delivers significant improvements on reasoning tasks, where existing models often rely on memorization and fail in zero-shot settings.

Problem

• Addresses time series reasoning limitations in LLMs
• Mitigates hallucination and knowledge leakage in analysis
• Enhances interpretability without multimodal alignment training

Innovation

• Uses atomic operators for raw time series interaction
• Employs iterative evidence gathering with self-critique
• Delegates statistical analysis to specialized tools