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Paper Citation Record · LEDGER

Modular Foundation Models for Time-Series Perception in Digital Twins

As of 8 August 2026, this Paper Citation Record lists 100 of 161 outbound references and 0 inbound Pith citation observations for arXiv:2607.03585.

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

pith.paper-citation-record.v1
2607.03585 v1

Coverage vector

measured 100 of 161 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T01:22:51.284207Z

measured 100 of 100 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 161 outbound references displayed

  • verified exact11
  • verified fuzzy0
  • unresolved89
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff411a3e-8e2b-4a33-96c9-7060928a7ac2 · outbound

This paper cites Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=.

Modular Foundation Models for Time-Series Perception in Digital Twins Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

Reference 1

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Observation efdfecae-9ba9-4cca-be98-9443136b7142 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , year=.

Modular Foundation Models for Time-Series Perception in Digital Twins IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

Reference 2

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:d2488096345699368114747429585ad7dea41b6f3017cd6c69f3b6ce9f81d78f

Observation fb955563-7c6a-4e8f-af18-694e15643e3c · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

Modular Foundation Models for Time-Series Perception in Digital Twins A decoder-only foundation model for time-series forecasting

Reference 3

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Observation 248c6cdb-6e62-4ddf-879a-5550174c84a6 · outbound

This paper cites ACM Computing Surveys (CSUR) , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins ACM Computing Surveys (CSUR) , volume=

Reference 4

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Observation 53b11659-fedd-44c1-9a09-662b6a7bce29 · outbound

This paper cites Sustainability , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins Sustainability , volume=

Reference 5

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Observation 7f73e1ad-b35c-4af6-852a-8140627988a7 · outbound

This paper cites PHM Soc Eur Conf.

Modular Foundation Models for Time-Series Perception in Digital Twins PHM Soc Eur Conf

Reference 6

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Observation 6ca22536-473a-4c92-9a7a-8bacd3e347e9 · outbound

This paper cites Journal of Computational Design and Engineering , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins Journal of Computational Design and Engineering , volume=

Reference 7

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Observation eb5e15c2-9107-4af5-a85e-388d8c1c4cef · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins IEEE Transactions on Pattern Analysis and Machine Intelligence , volume=

Reference 8

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Observation 767905fa-3b14-4241-8164-9bc7abea59e2 · outbound

This paper cites Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts

Reference 9

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Observation ce02707e-23b5-466d-91ce-14690d994e5e · outbound

This paper cites Learning to Route Among Specialized Experts for Zero-Shot Generalization.

Modular Foundation Models for Time-Series Perception in Digital Twins Learning to Route Among Specialized Experts for Zero-Shot Generalization

Reference 10

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Observation 91352f03-be56-4255-a2b6-984aae666eee · outbound

This paper cites Self-Supervised Learning for Time Series: Contrastive or Generative?.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-Supervised Learning for Time Series: Contrastive or Generative?

Reference 11

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Observation cb0013c5-978b-4656-92dd-752269bc982f · outbound

This paper cites Information Sciences , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins Information Sciences , volume=

Reference 12

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Observation a31f97c8-9617-4477-b34e-59af9b06453e · outbound

This paper cites Proceedings of Neural Information Processing Systems, NeurIPS , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Proceedings of Neural Information Processing Systems, NeurIPS , year =

Reference 13

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Observation 8c158a42-4004-47a3-8fd3-9a3fe727d234 · outbound

This paper cites Unified Training of Universal Time Series Forecasting Transformers.

Modular Foundation Models for Time-Series Perception in Digital Twins Unified Training of Universal Time Series Forecasting Transformers

Reference 14

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Observation f8763ff8-ac2d-4697-9bd4-67922925deaf · outbound

This paper cites Preprint , year=.

Modular Foundation Models for Time-Series Perception in Digital Twins Preprint , year=

Reference 15

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Observation 0a7a94ab-017f-487f-8511-493d4fd2a588 · outbound

This paper cites UniTS: A Unified Multi-Task Time Series Model.

Modular Foundation Models for Time-Series Perception in Digital Twins UniTS: A Unified Multi-Task Time Series Model

Reference 16

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Observation 8301df50-8136-4f8c-b79c-84a327973aff · outbound

This paper cites TimeGPT-1.

Modular Foundation Models for Time-Series Perception in Digital Twins TimeGPT-1

Reference 17

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Observation 14f7d59a-bdd6-4f59-97f3-d109a29fcd34 · outbound

This paper cites Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining , pages=.

Modular Foundation Models for Time-Series Perception in Digital Twins Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining , pages=

Reference 18

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Observation 36c3d190-b23b-49b1-a6ca-f7675a54a426 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 19

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Observation b877ef9f-d453-453a-9eae-a3aec5ca54cd · outbound

This paper cites arXiv preprint arXiv:2303.00320 , year=.

Modular Foundation Models for Time-Series Perception in Digital Twins arXiv preprint arXiv:2303.00320 , year=

Reference 20

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Observation 9bd8879c-398b-4b34-b0b0-b1d7c1afb5f1 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins Advances in Neural Information Processing Systems , volume=

Reference 21

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Observation 0905f67f-f1af-4e7a-830c-5721ff21b222 · outbound

This paper cites IEEE transactions on pattern analysis and machine intelligence , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins IEEE transactions on pattern analysis and machine intelligence , volume=

Reference 22

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Observation 4b627e3d-08bb-411c-92f3-e1871bfa2121 · outbound

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Modular Foundation Models for Time-Series Perception in Digital Twins IEEE transactions on knowledge and data engineering , volume=

Reference 23

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Observation a5ceba3d-93e3-43a3-acbc-2278ca41683c · outbound

This paper cites Chronos: Learning the Language of Time Series.

Modular Foundation Models for Time-Series Perception in Digital Twins Chronos: Learning the Language of Time Series

Reference 24

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Observation 4d7400df-ad04-40df-b8fd-771a13c649cb · outbound

This paper cites 2024 , note =.

Modular Foundation Models for Time-Series Perception in Digital Twins 2024 , note =

Reference 25

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Observation 97438f80-b936-4340-9192-ec122ba02e49 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins Advances in Neural Information Processing Systems , volume=

Reference 26

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Observation 70884b6d-4858-4391-9c76-2302a6b6ee05 · outbound

This paper cites Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining , pages=.

Modular Foundation Models for Time-Series Perception in Digital Twins Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining , pages=

Reference 27

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Observation 15025d7b-89af-46c1-b74b-e5e26c86e434 · outbound

This paper cites Time-Series Representation Learning via Temporal and Contextual Contrasting.

Modular Foundation Models for Time-Series Perception in Digital Twins Time-Series Representation Learning via Temporal and Contextual Contrasting

Reference 28

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Observation 8be5f3ae-0459-4037-b827-ea0a7719b098 · outbound

This paper cites TimeNet: Pre-trained deep recurrent neural network for time series classification.

Modular Foundation Models for Time-Series Perception in Digital Twins TimeNet: Pre-trained deep recurrent neural network for time series classification

Reference 29

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Observation 2eff7767-dfd1-4ac4-b6e1-6b722f5660d6 · outbound

This paper cites Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization.

Modular Foundation Models for Time-Series Perception in Digital Twins Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization

Reference 30

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Observation 16c45812-9f5f-4dc1-bacd-9f97e7208b34 · outbound

This paper cites ACM Transactions on Intelligent Systems and Technology (TIST) , volume=.

Modular Foundation Models for Time-Series Perception in Digital Twins ACM Transactions on Intelligent Systems and Technology (TIST) , volume=

Reference 31

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Observation 946e0c05-46a8-4c21-879f-d4196491c855 · outbound

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Modular Foundation Models for Time-Series Perception in Digital Twins Unresolved cited work

Reference 32

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Observation fc73694b-a112-4977-a7a9-f7911227aee1 · outbound

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Modular Foundation Models for Time-Series Perception in Digital Twins IEEE Transactions on Neural Networks and Learning Systems , year=

Reference 33

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Observation fa6d3ae5-9c1f-4de3-b121-4cab615e9cfd · outbound

This paper cites Unsupervised Representation Learning for Time Series: A Review.

Modular Foundation Models for Time-Series Perception in Digital Twins Unsupervised Representation Learning for Time Series: A Review

Reference 34

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Modular Foundation Models for Time-Series Perception in Digital Twins 7th international Mardin Artuklu scientific research conference , pages=

Reference 35

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Observation 68d2ce54-a742-4bd0-814e-4d02f1d001eb · outbound

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Modular Foundation Models for Time-Series Perception in Digital Twins Artificial Intelligence Review , volume=

Reference 36

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Modular Foundation Models for Time-Series Perception in Digital Twins Exploring the power of ChatGPT: Applications, techniques, and implications , pages=

Reference 37

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Modular Foundation Models for Time-Series Perception in Digital Twins 2025 , url =

Reference 38

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Observation ab9734a3-a2de-4f63-a239-226528a114ff · outbound

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Modular Foundation Models for Time-Series Perception in Digital Twins Proceedings of the 35th AAAI Conference on Artificial Intelligence , year =

Reference 39

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Modular Foundation Models for Time-Series Perception in Digital Twins , title =

Reference 40

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Modular Foundation Models for Time-Series Perception in Digital Twins 2020 , howpublished =

Reference 41

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Reference 42

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Reference 43

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Modular Foundation Models for Time-Series Perception in Digital Twins 2020 , url =

Reference 44

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Observation 39d479d3-cce2-4001-a892-b50d30225a95 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Modular Foundation Models for Time-Series Perception in Digital Twins Representation Learning with Contrastive Predictive Coding

Reference 46

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This paper cites 2021 , isbn =.

Modular Foundation Models for Time-Series Perception in Digital Twins 2021 , isbn =

Reference 47

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Observation c87a07a9-9332-4159-b20c-0f8900f2d1c0 · outbound

This paper cites Learning Disentangled Representations with Semi-Supervised Deep Generative Models.

Modular Foundation Models for Time-Series Perception in Digital Twins Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Reference 48

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This paper cites Modular Deep Learning.

Modular Foundation Models for Time-Series Perception in Digital Twins Modular Deep Learning

Reference 49

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Observation d394721d-88dd-4250-8eac-bea2301add98 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Modular Foundation Models for Time-Series Perception in Digital Twins On the Opportunities and Risks of Foundation Models

Reference 50

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Observation d4caf758-8a8e-4135-bb75-4d0783e90d91 · outbound

This paper cites A General Purpose Neural Architecture for Geospatial Systems.

Modular Foundation Models for Time-Series Perception in Digital Twins A General Purpose Neural Architecture for Geospatial Systems

Reference 51

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Observation 83910d4d-20e3-4fa2-96a9-36bf84d49b4d · outbound

This paper cites Neural Attentive Circuits.

Modular Foundation Models for Time-Series Perception in Digital Twins Neural Attentive Circuits

Reference 52

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Observation a86f0b7f-4591-4598-b64c-a74738a1c328 · outbound

This paper cites Perceiver IO: A General Architecture for Structured Inputs & Outputs.

Modular Foundation Models for Time-Series Perception in Digital Twins Perceiver IO: A General Architecture for Structured Inputs & Outputs

Reference 53

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Observation ade1653d-893f-4357-8b7d-b865bddf7d8d · outbound

This paper cites Towards artificial general intelligence via a multimodal foundation model , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Towards artificial general intelligence via a multimodal foundation model , year =

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Observation 71e0f9b9-2c33-4238-921c-ac420ee6dd7a · outbound

This paper cites Plex: Towards Reliability using Pretrained Large Model Extensions.

Modular Foundation Models for Time-Series Perception in Digital Twins Plex: Towards Reliability using Pretrained Large Model Extensions

Reference 55

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Observation 7eafcd02-8e93-49d1-8ef0-648dddd928a5 · outbound

This paper cites Multi-Task Learning with Deep Neural Networks: A Survey.

Modular Foundation Models for Time-Series Perception in Digital Twins Multi-Task Learning with Deep Neural Networks: A Survey

Reference 56

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Observation 571b6bb9-57a6-4763-b30c-871f8f964522 · outbound

This paper cites Multi-Task Learning for Dense Prediction Tasks: A Survey , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Multi-Task Learning for Dense Prediction Tasks: A Survey , year =

Reference 57

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Observation 46718a98-248f-4b7a-8883-04218391b9a5 · outbound

This paper cites and Mann, Matthias , journal =.

Modular Foundation Models for Time-Series Perception in Digital Twins and Mann, Matthias , journal =

Reference 58

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Observation ab6cdbe0-afa5-4fa9-878d-0aa42d6bc00b · outbound

This paper cites Advances in Neural Information Processing Systems , title =.

Modular Foundation Models for Time-Series Perception in Digital Twins Advances in Neural Information Processing Systems , title =

Reference 59

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Observation 99f950b3-fd7e-4647-adf3-d027c90fad7d · outbound

This paper cites Denoising Task Routing for Diffusion Models.

Modular Foundation Models for Time-Series Perception in Digital Twins Denoising Task Routing for Diffusion Models

Reference 60

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Observation 7a9ab267-c335-4ae3-8d49-d98891eb73fb · outbound

This paper cites Soft Merging of Experts with Adaptive Routing.

Modular Foundation Models for Time-Series Perception in Digital Twins Soft Merging of Experts with Adaptive Routing

Reference 61

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Observation d23fef1f-f398-4547-8d0a-be29a9d47ee9 · outbound

This paper cites Salman Asif , title =.

Modular Foundation Models for Time-Series Perception in Digital Twins Salman Asif , title =

Reference 62

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Observation 76467fdb-d6a3-4b14-91f0-d33f8b839708 · outbound

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Modular Foundation Models for Time-Series Perception in Digital Twins 2023 , month = Dec, type =

Reference 63

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Observation 5a67480f-b8b8-482f-8c1a-d6789b8b0aaf · outbound

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Modular Foundation Models for Time-Series Perception in Digital Twins Towards Causal Representation Learning

Reference 64

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Observation 9f168ee3-f3e9-4340-9ead-7164df3bc6d4 · outbound

This paper cites Inductive Biases for Deep Learning of Higher-Level Cognition.

Modular Foundation Models for Time-Series Perception in Digital Twins Inductive Biases for Deep Learning of Higher-Level Cognition

Reference 65

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Observation e2714652-4f26-40ca-94ff-2d2e23fbfafb · outbound

This paper cites Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning.

Modular Foundation Models for Time-Series Perception in Digital Twins Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning

Reference 66

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Observation 5be67352-17f4-4177-872c-7b6171cf9c44 · outbound

This paper cites A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT.

Modular Foundation Models for Time-Series Perception in Digital Twins A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

Reference 67

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Observation 4100ea11-9eb2-402e-b5b5-cf0b184a0899 · outbound

This paper cites Pretrained Language Models for Text Generation: A Survey.

Modular Foundation Models for Time-Series Perception in Digital Twins Pretrained Language Models for Text Generation: A Survey

Reference 68

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Observation 2c800444-f13d-4d33-996d-e332cb6ba061 · outbound

This paper cites Data Efficient Masked Language Modeling for Vision and Language.

Modular Foundation Models for Time-Series Perception in Digital Twins Data Efficient Masked Language Modeling for Vision and Language

Reference 69

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Observation 930c73dd-9adc-49fa-80e2-b713f9023fc3 · outbound

This paper cites and Healy, Graham and Smeaton, Alan F.

Modular Foundation Models for Time-Series Perception in Digital Twins and Healy, Graham and Smeaton, Alan F

Reference 70

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Observation 25e8bb8a-1dc9-4bea-a3b2-54326e975382 · outbound

This paper cites A Theoretical Analysis of Contrastive Unsupervised Representation Learning , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins A Theoretical Analysis of Contrastive Unsupervised Representation Learning , year =

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Observation cca2a1b8-fb83-4ded-b5a2-bf84ea822ec9 · outbound

This paper cites Martinez and Juan Manuel Gorriz , journal =.

Modular Foundation Models for Time-Series Perception in Digital Twins Martinez and Juan Manuel Gorriz , journal =

Reference 72

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Source-reported events for the cited work

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Observation 7dda7f61-48c4-4b69-a551-d6c445f7a4e1 · outbound

This paper cites Deep multimodal fusion for semantic image segmentation: A survey , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Deep multimodal fusion for semantic image segmentation: A survey , year =

Reference 73

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Observation 05e45de2-5384-4acc-a3e9-e4eb1032d901 · outbound

This paper cites Attention Bottlenecks for Multimodal Fusion , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Attention Bottlenecks for Multimodal Fusion , year =

Reference 74

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Observation 13592099-9db3-48b5-a82a-a2e160c58bf1 · outbound

This paper cites and Marks, Tim K.

Modular Foundation Models for Time-Series Perception in Digital Twins and Marks, Tim K

Reference 75

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Observation bee91df4-d612-4632-a80c-f5e3dae6fdd4 · outbound

This paper cites Multi-source heterogeneous data fusion , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Multi-source heterogeneous data fusion , year =

Reference 76

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Observation 67bb353d-41b4-44fb-a122-1e0386f1cf5b · outbound

This paper cites Lifting the Curse of Multilinguality by Pre-training Modular Transformers , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Lifting the Curse of Multilinguality by Pre-training Modular Transformers , year =

Reference 77

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Observation 31c69371-9827-4eb5-93ed-90e3e9f2616b · outbound

This paper cites Beyond English-Centric Multilingual Machine Translation , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Beyond English-Centric Multilingual Machine Translation , year =

Reference 78

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Observation 587494f5-7702-49e2-9591-cb6411ba6e79 · outbound

This paper cites and Zettlemoyer, Luke , booktitle =.

Modular Foundation Models for Time-Series Perception in Digital Twins and Zettlemoyer, Luke , booktitle =

Reference 79

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Observation f601a8d1-a5d2-4f76-965d-e75617e19f10 · outbound

This paper cites Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models.

Modular Foundation Models for Time-Series Perception in Digital Twins Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models

Reference 80

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Observation 1822caad-65ea-4ce5-b078-dc401529bd68 · outbound

This paper cites Modular Networks: Learning to Decompose Neural Computation , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Modular Networks: Learning to Decompose Neural Computation , year =

Reference 81

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:8911988c8726f1b48ef40c99d6a2fcd5ac0ed28e8726f820ae30559dac7b1b08

Observation a38b9f1a-f99a-48d3-b568-7cb90b0ddc87 · outbound

This paper cites An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems.

Modular Foundation Models for Time-Series Perception in Digital Twins An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems

Reference 82

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:9f07db324cfb9d892909262ececd08471450af095e747d8a209944f6212f972b

Observation 71e9ee68-0671-4dcf-a58c-f747c6e7fd47 · outbound

This paper cites Combining Modular Skills in Multitask Learning.

Modular Foundation Models for Time-Series Perception in Digital Twins Combining Modular Skills in Multitask Learning

Reference 83

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no resolver link, observed 2026-07-12T01:22:51.284207Z

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:37618b450393aa4cdda37f9548ef93964e223457e10e929bf238c5d52a2474c5

Observation 224a988c-29e6-4d79-a67f-eaf25ff51bd4 · outbound

This paper cites Multi-Head Adapter Routing for Cross-Task Generalization.

Modular Foundation Models for Time-Series Perception in Digital Twins Multi-Head Adapter Routing for Cross-Task Generalization

Reference 84

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no resolver link, observed 2026-07-12T01:22:51.284207Z

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:444872f481b0d94c2f5ba9b60d6da0f82e2627eb0d7e1cddc971f1897da56907

Observation 8a14bd4e-4133-41c7-b4ba-9a20f9e0fda2 · outbound

This paper cites Supermasks in Superposition , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Supermasks in Superposition , year =

Reference 85

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:49a15107d99eb51dc88ac63e47c6ba7326cf69e6ddf1cbd3b7546ea123e5bd09

Observation e1e8b456-ce5e-4fa0-9a7d-6f5cb43909b0 · outbound

This paper cites 2021 , url =.

Modular Foundation Models for Time-Series Perception in Digital Twins 2021 , url =

Reference 86

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:9f8223db09e1fa0ecd1a9041dfda99cba04128c474c97013eff1c2ac7325e204

Observation 3086cdc2-037c-4355-8046-8ffae4a4e259 · outbound

This paper cites End-to-End Multi-Task Learning with Attention.

Modular Foundation Models for Time-Series Perception in Digital Twins End-to-End Multi-Task Learning with Attention

Reference 87

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:6e7dac1209b2fbcd1dd0e2e26a10aaf2f3e1ccf97a44effaaf48cef6826d2a28

Observation fb384e8b-4982-4e3a-8fa2-3b9d6dcce758 · outbound

This paper cites Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics.

Modular Foundation Models for Time-Series Perception in Digital Twins Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Reference 88

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no resolver link, observed 2026-07-12T01:22:51.284207Z

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:63d2f6bd3e8d6888e717df44b210ae0f26133095e4ec224b1c47d8e8e0663e11

Observation 0a74c32c-4da4-4e87-a089-1a16cda0a56f · outbound

This paper cites Self-Supervised Learning of Graph Neural Networks: A Unified Review , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-Supervised Learning of Graph Neural Networks: A Unified Review , year =

Reference 89

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verified exact
arxiv_id, observed 2026-07-12T01:28:23.459030Z

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.

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:0ecf7bea48b8ef31458d1a4db67dd316c14b3e53955f5f06c83e8fbf6b7f1864

Observation dc47fd1b-696b-4eeb-b270-6ba8c5f77a63 · outbound

This paper cites Self-Supervised Learning for Recommender Systems: A Survey , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-Supervised Learning for Recommender Systems: A Survey , year =

Reference 90

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verified exact
arxiv_id, observed 2026-07-12T01:28:23.476218Z

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.

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:db0fc624107cca3ed5332d0f2715652cae5ca062dbf68671cca50268ec854a6b

Observation 259fb630-f257-4c5a-bce4-892dd501333a · outbound

This paper cites Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation , year =

Reference 91

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verified exact
arxiv_id, observed 2026-07-12T01:28:23.104704Z

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.

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:13a2832c811693855e619ba1ec49d73f4103fa35c8750e6ef05d0b38ecc5d421

Observation 4ee33be6-8b44-45ca-84fb-3ad94c6079e5 · outbound

This paper cites , journal =.

Modular Foundation Models for Time-Series Perception in Digital Twins , journal =

Reference 92

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:d4bacd842e466194ab15a5ccf4c250fa22dcbecc2d288da29ce2ced7473a6eec

Observation 95a1cf12-4c5e-460f-94c5-a8c7e5220b33 · outbound

This paper cites Graph-Based Contrastive Learning for Description and Detection of Local Features , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Graph-Based Contrastive Learning for Description and Detection of Local Features , year =

Reference 93

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verified exact
arxiv_id, observed 2026-07-12T01:28:23.394957Z

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

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:4961e8478cfba3e00aec8dbf05fca8d1840756ee69f03559d44d0fcdef33acdc

Observation 5ca502b4-9e7d-490b-ace2-f0fe3a48e15a · outbound

This paper cites , journal =.

Modular Foundation Models for Time-Series Perception in Digital Twins , journal =

Reference 94

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:f92a839a7fbc934a2843a5e1b4fcde4f94152d3a2e7d1909212390baa6b56b2f

Observation 1f6e5377-69a8-4fff-bf94-efe6897d87e4 · outbound

This paper cites Self-supervised graph representations with generative adversarial learning , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-supervised graph representations with generative adversarial learning , year =

Reference 95

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verified exact
arxiv_id, observed 2026-07-12T01:28:23.093419Z

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.

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:8ecdfc367074b906b41a0f9498cc8ff7594f194f140ba50d2d8a05aa5256d689

Observation ff4f0a85-7e6d-49ca-b486-e0e1322a384c · outbound

This paper cites and Braham, Nassim Ait Ali and Mou, Lichao and Zhu, Xiao Xiang , journal =.

Modular Foundation Models for Time-Series Perception in Digital Twins and Braham, Nassim Ait Ali and Mou, Lichao and Zhu, Xiao Xiang , journal =

Reference 96

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:d45e68af5b8ee372e7eb228e3f1c4225952911687d4d7bd5c97e43d91f9b8e3a

Observation 7b335466-9ac6-4791-ba0e-6b336d498fdb · outbound

This paper cites Self-Supervised Learning: Generative or Contrastive , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Self-Supervised Learning: Generative or Contrastive , year =

Reference 97

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:4ab0f008f80d6cd0019ec357d5e579425b2c5708429c57cd3fdbee27f27656f8

Observation 6b59ac72-6529-451a-bfd5-0da2be784d14 · outbound

This paper cites A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions , year =

Reference 98

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:aa8e18fa8c4149c4883c1ec0a94525b060dc030b7a8af0236e4d81f95f788765

Observation ff0a8888-823e-46d9-934f-79a5f3de99c3 · outbound

This paper cites Simple and Deep Graph Convolutional Networks , year =.

Modular Foundation Models for Time-Series Perception in Digital Twins Simple and Deep Graph Convolutional Networks , year =

Reference 99

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:dadf02e390a168b3e4eafad516852b335bac15ad415085817ddbc5579bfdb43a

Observation b756456b-f95e-4ca5-98fa-f23c778ec278 · outbound

This paper cites Ben , journal =.

Modular Foundation Models for Time-Series Perception in Digital Twins Ben , journal =

Reference 100

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verified exact
doi, observed 2026-07-12T01:28:22.964828Z

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.

source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:b2dc724f75f16924bfac885484fa904ce780d29f15441d6aa5e04839cb8da7ab

Observation cd0806f2-b384-411b-90ad-bb7d407074fa · outbound

This paper cites Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey.

Modular Foundation Models for Time-Series Perception in Digital Twins Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey

Reference 101

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source=arxiv_source observed=2026-07-12T01:22:51.284207Z digest=sha256:cb64b414fd1cee5ca81ff68287f2553f876309039574b06562e0013156881219

Pith citing papers

No inbound Pith citation observations are available.