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Abstract: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.
| Comments: | Workshop version of this paper published at ICLR TSALM 2026. Benchmark generation code and datasets: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.14200 [cs.LG] |
| (or arXiv:2602.14200v5 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.14200 arXiv-issued DOI via DataCite |
From: Nicolas Zumarraga [view email]
[v1]
Sun, 15 Feb 2026 15:50:02 UTC (3,737 KB)
[v2]
Sun, 1 Mar 2026 11:02:52 UTC (3,737 KB)
[v3]
Thu, 19 Mar 2026 15:39:56 UTC (3,734 KB)
[v4]
Sun, 12 Apr 2026 18:32:27 UTC (3,737 KB)
[v5]
Tue, 12 May 2026 18:35:23 UTC (3,969 KB)
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