Paper Citation Record · LEDGER

Can LLMs Understand Time Series Anomalies?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2410.05440.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.05440 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:52:49.656206Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-19T09:32:15.986965Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f54707cf-f5cf-4b75-9312-bcba300a84a1 · inbound

From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.

From Time Series Analysis to Question Answering: A Survey in the LLM Era Can LLMs Understand Time Series Anomalies?

Reference 134

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:32:15.992050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1f53289-5778-4dea-9a20-109edb928886 · inbound

Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons cites this paper.

Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons Can LLMs Understand Time Series Anomalies?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T21:52:49.656206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a67bb95-796a-4551-96c0-414aa0b736c7 · inbound

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications cites this paper.

Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications Can LLMs Understand Time Series Anomalies?

Reference 238

Resolution: unresolved; no resolver link, observed 2026-08-06T21:07:11.498916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f19df82-b1b2-469a-be7b-8e659e76b8bb · inbound

Towards Interpretable Time Series Foundation Models cites this paper.

Towards Interpretable Time Series Foundation Models Can LLMs Understand Time Series Anomalies?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T18:44:42.080260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b01ee4ac-b3e9-477a-8bf0-c2c7fd63ae0c · inbound

Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback cites this paper.

Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback Can LLMs Understand Time Series Anomalies?

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:32:01.457444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fc5a217d-53ba-4d4e-8df1-f9a568f746fd · inbound

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection cites this paper.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Can LLMs Understand Time Series Anomalies?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T03:12:07.557091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e67a17e-640d-4105-b2f6-acd6f52fba1d · inbound

HEARTS: Benchmarking LLM Reasoning on Health Time Series cites this paper.

HEARTS: Benchmarking LLM Reasoning on Health Time Series Can LLMs Understand Time Series Anomalies?

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-02T21:02:33.091583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87f31390-9b50-4cb4-b84c-2b6a1b2eaed0 · inbound

Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Management cites this paper.

Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Management Can LLMs Understand Time Series Anomalies?

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T02:47:20.596701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.