arXiv cs.AI· Shunyu Wu, Dan Li, Haozheng Ye, Weibin Feng, Jian Lou, Bo Zhang, Wenjie Feng, Chenjuan Guo, See-Kiong Ng·· 4 小时前AI 评分41
TSQAgent：基于专属智能体推理的时间序列数据质量评级方法（NeurIPS 2026 Spotlight）
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
AI 导读
研究者提出 TSQAgent，一个用于时间序列数据质量评级的智能体推理框架，包含 Perceiver、Inspector、Adjudicator 三个协作角色，并构建了 TSQBench 基准评测 LLM 的质量维度识别与量化比较能力。
正文 · 原文
Abstract:Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs) have emerged as a promising paradigm for TS quality assessment via pairwise comparison and per-dimension evaluation. However, existing approaches rely on manually predefined quality dimensions and purely text-based reasoning, leaving it unknown whether LLMs can identify truly relevant quality dimensions or perform grounded and quantitative quality comparisons. To investigate this, we construct TSQBench, a dedicated benchmark for evaluating LLMs on two progressive capabilities: (i) understanding and identifying relevant quality dimensions, and (ii) performing quality comparison under specific dimensions. Our analysis reveals that current LLMs consistently struggle with both dimension identification and evidence-grounded quality comparison. To address these limitations, we propose TSQAgent, a novel agentic reasoning framework for TS quality rating consisting of three collaborative roles: Perceiver for focused dimension selection, Inspector for dimension-wise quantitative analysis, and Adjudicator that aggregates and refines the final judgment. In particular, we introduce an agentic reasoning strategy that instills the ability to identify and prioritize the most relevant quality dimensions, and further propose an agent workflow equipped with external analytical tools to enable precise quantitative comparisons over selected dimensions. Experiments on both the proposed benchmark and eleven real-world datasets demonstrate that our framework not only substantially improves LLMs' capabilities in quality understanding and quantitative comparison but also effectively translates these improvements into better quality-aware data selection, leading to enhanced downstream performance and data efficiency.
| Comments: | Accepted at NeurIPS 2026 (Spotlight) |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.03629 [cs.AI] |
| (or arXiv:2606.03629v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.03629 arXiv-issued DOI via DataCite |
Submission history
From: Shunyu Wu [view email]
[v1]
Tue, 2 Jun 2026 13:28:17 UTC (3,647 KB)
[v2]
Thu, 5 Nov 2026 02:51:47 UTC (11,498 KB)
来源：arXiv cs.AI · arxiv.org