TS-Haystack：一种用于长上下文时间序列推理的多任务检索基准
TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning
AI总结：
本文提出TS-Haystack基准，评估长上下文时间序列推理能力，发现现有TSLMs存在长上下文退化问题，代理检索框架在9/10任务中表现优异。
摘要：
时间序列语言模型（TSLMs）承诺对现实世界时间数据进行推理，但其在长时间序列中的检索和推理能力仍缺乏测试。我们引入TS-Haystack，一个包含10个基于事件的问题回答任务的多领域检索基准，涵盖从100秒到24小时的上下文，涉及直接检索、时间推理、多步推理和上下文异常检测。现有TSLMs表现出严重的长上下文退化：准确性随上下文长度下降，直接令牌化模型在高频率信号上内存不足，时间区间任务在增加时间序列长度时接近零准确性。使用专门的时间序列分类器工具的代理检索框架在9/10任务中匹配或优于现有最佳TSLMs，凸显代理检索作为长上下文TSLMs的有前途方法。
英文摘要：
Time Series Language Models (TSLMs) promise reasoning over real-world temporal data, but their ability to retrieve and reason over long time-series remains largely untested. We introduce TS-Haystack, a multi-domain retrieval benchmark with ten event-grounded question-answering tasks over contexts from 100 seconds to 24 hours, spanning direct retrieval, temporal reasoning, multi-step reasoning, and contextual anomaly detection. Existing TSLMs exhibit severe long-context degradation: accuracy declines with context length, direct-tokenization models run out of memory beyond 100 seconds on high-rate signals, and time-interval-grounded tasks collapse toward near-zero accuracy when increasing the time-series lengths, aligning with existing literature on text and multi-modal long context retrieval. An agentic retrieval framework using specialized time-series classifier tools matches or outperforms SoTA TSLMs on 9 of 10 tasks, highlighting agentic retrieval as a promising approach for long-context TSLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
