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Paper Citation Record · LEDGER

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2602.08868.

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

pith.paper-citation-record.v1
2602.08868 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:12:07.721174Z

measured 25 of 25 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

25 of 25 outbound references displayed

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Outbound references

Observation 002378d5-cf9d-474f-baad-b17a30981d67 · outbound

This paper cites Large language models can be zero-shot anomaly detectors for time series?.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Large language models can be zero-shot anomaly detectors for time series?

Reference 1

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Observation 00c347ca-1562-4c62-9747-3523d7232d83 · outbound

This paper cites Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm.KI-2012: poster and demo track, 1:59–63,.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm.KI-2012: poster and demo track, 1:59–63,

Reference 5

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Observation 816498b0-f978-4af2-96a4-4e81319ae820 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 7

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Observation aaa00cd6-5240-4417-bc9d-7c8c7cb1ee0b · outbound

This paper cites Time-R1: Towards Comprehensive Temporal Reasoning in LLMs.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Time-R1: Towards Comprehensive Temporal Reasoning in LLMs

Reference 11

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source=pdf_text observed=2026-08-03T03:12:06.372058Z digest=sha256:1ece333623e0a14c49c105e3a576a05181fd0edcb0d4c9933d6b67a9ec514bda

Observation 1b6e4509-3622-4a43-bed7-ff6aaf4f734d · outbound

This paper cites TAB: Unified Benchmarking of Time Series Anomaly Detection Methods.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

Reference 12

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source=pdf_text observed=2026-08-03T03:12:06.460082Z digest=sha256:336a8d70ffb3cff89c645f0a2084e03eac431ae8f96823165cf5a2900703c5a0

Observation 5ea877bb-6c2b-4ee7-bca7-65ca39cb2541 · outbound

This paper cites Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders

Reference 14

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Observation eb9da44e-a220-4d8f-a410-eb1ea72d9b9b · outbound

This paper cites Inferring Events from Time Series using Language Models.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Inferring Events from Time Series using Language Models

Reference 15

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Observation 00fee543-11b3-470a-af74-9de3c2a8eafe · outbound

This paper cites SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution

Reference 16

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Observation 76835bd2-31fc-4289-b4a6-910f10bdcd17 · outbound

This paper cites ChatTS: Aligning time series with LLMs via synthetic data for enhanced understanding and reasoning.arXiv preprint arXiv:2412.03104,.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection ChatTS: Aligning time series with LLMs via synthetic data for enhanced understanding and reasoning.arXiv preprint arXiv:2412.03104,

Reference 17

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Observation 5117bcb9-30d5-4f28-b876-a45278743193 · outbound

This paper cites Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Reference 18

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Observation 79a7e3bd-116b-4c2f-8db8-696779762450 · outbound

This paper cites Can multimodal LLMs perform time series anomaly detection?arXiv preprint arXiv:2502.17812,.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Can multimodal LLMs perform time series anomaly detection?arXiv preprint arXiv:2502.17812,

Reference 19

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Observation f6bccc65-842f-4169-8741-2e191938fab5 · outbound

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

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback

Reference 20

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Observation 3268303b-8b2e-4543-8acd-c5bf183fe7d3 · outbound

This paper cites Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets

Reference 21

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Observation 890d4d18-5614-414b-b66e-3259c3b19506 · outbound

This paper cites See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers

Reference 24

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Observation 6a086772-d867-4da4-b1e0-ba9941a5bbe9 · outbound

This paper cites 3https://github.com/langfengQ/TimeMaster 17 Preprint.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection 3https://github.com/langfengQ/TimeMaster 17 Preprint

Reference 25

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Observation 6b7518a6-ae7c-4d88-ad5d-dc9fa66f2e96 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 1999

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Observation e1729f09-bae7-44f3-a856-331bf39990bd · outbound

This paper cites A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization

Reference 2008

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Observation f3216b4d-24db-4aba-b58b-c85bbb70a494 · outbound

This paper cites Synergizing large language models and task-specific models for time series anomaly detection.arXiv preprint arXiv:2501.05675,.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Synergizing large language models and task-specific models for time series anomaly detection.arXiv preprint arXiv:2501.05675,

Reference 2010

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Observation 2adc1764-6968-4d4b-915c-df6be3b6d493 · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection MOMENT: A Family of Open Time-series Foundation Models

Reference 2012

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Observation 4d1044aa-e866-479a-9a07-cde45441b839 · outbound

This paper cites TimesBERT: A BERT-Style Foundation Model for Time Series Understanding.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection TimesBERT: A BERT-Style Foundation Model for Time Series Understanding

Reference 2016

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Observation a451aa46-8136-4304-add7-3781591c8897 · outbound

This paper cites Position: Empowering time series reasoning with multi- modal LLMs.arXiv preprint arXiv:2502.01477,.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Position: Empowering time series reasoning with multi- modal LLMs.arXiv preprint arXiv:2502.01477,

Reference 2018

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Observation 0bcba3f0-4dec-45e3-a257-083d314d8db5 · outbound

This paper cites Displacement interpolation using lagrangian mass transport.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Displacement interpolation using lagrangian mass transport

Reference 2021

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

This paper cites Can LLMs Understand Time Series Anomalies?.

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

Reference 2023

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source=pdf_text observed=2026-08-03T03:12:07.557091Z digest=sha256:17658bddce0d4b08272f3947df3e256d40ba0bccbb59fec89c4b0c36926f62b2

Observation cbd01212-7fce-446d-949b-349a299b89ad · outbound

This paper cites Qwen2.5-VL Technical Report.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Qwen2.5-VL Technical Report

Reference 2024

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source=pdf_text observed=2026-08-03T03:12:03.787315Z digest=sha256:9053c6082fd20eab8ec8e0d0f693d13c2f7ede33dc1c39f69098759e6d0eaac2

Observation 4a667ddb-5883-41f6-a63b-6bc635e61f3d · outbound

This paper cites Harnessing vision-language models for time series anomaly detection.arXiv preprint arXiv:2506.06836,.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Harnessing vision-language models for time series anomaly detection.arXiv preprint arXiv:2506.06836,

Reference 2025

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Pith citing papers

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