MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling | Paper Citation Record

Observation 74bd4d9a-7202-415f-a856-445d11076a25 · outbound

This paper cites Time-IMM: A dataset and benchmark for irregular multimodal multivariate time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Time-IMM: A dataset and benchmark for irregular multimodal multivariate time series

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:17.012979Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 595af3d1-01ae-4b18-8e1f-5bc103107629 · outbound

This paper cites Improving medical predictions by irregular multimodal electronic health records modeling.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Improving medical predictions by irregular multimodal electronic health records modeling

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:17.021638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bdfcce36-e472-47d4-981c-f3ec3c8258ec · outbound

This paper cites Multimodal language models for financial forecasting from interleaved sequences of text and time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Multimodal language models for financial forecasting from interleaved sequences of text and time series

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:17.017391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 53f1efb6-66d9-499e-ae3b-4ba78ff9b786 · outbound

This paper cites A Survey of AIOps for Failure Management in the Era of Large Language Models.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling A Survey of AIOps for Failure Management in the Era of Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.826839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d8a9096-72e8-4cf1-be71-ebfac016fba3 · outbound

This paper cites Mimic-iv, a freely accessible electronic health record dataset.Scientific data, 10(1):1.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Mimic-iv, a freely accessible electronic health record dataset.Scientific data, 10(1):1

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:17.030391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0916c105-7f3f-4d28-9972-9001cafa73b0 · outbound

This paper cites MIMIC-IV.PhysioNet, October 2024.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling MIMIC-IV.PhysioNet, October 2024

Reference 6

Resolution: verified exact; doi, observed 2026-05-14T20:17:55.599255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55e2c8d1-3c84-444e-aa95-333c0513afea · outbound

This paper cites The eicu collaborative research database, a freely available multi-center database for critical care research.Scientific data, 5(1):180178.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling The eicu collaborative research database, a freely available multi-center database for critical care research.Scientific data, 5(1):180178

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.928639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ab729b58-598d-4f27-a54d-530e7a1e35db · outbound

This paper cites L.et al.Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals.circulation101, e215–e220 (2000).

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling L.et al.Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals.circulation101, e215–e220 (2000)

Reference 8

Resolution: metadata mismatch; doi, observed 2026-05-14T20:17:55.595374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec642042-7543-4fa4-936f-c0db1ce1b681 · outbound

This paper cites Using clinical notes with time series data for icu management.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Using clinical notes with time series data for icu management

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.915220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 38ab149d-bed5-470f-8355-79df41981bbc · outbound

This paper cites Fusemoe: Mixture-of-experts transformers for fleximodal fusion.Advances in Neural Information Processing Systems, 37: 67850–67900.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Fusemoe: Mixture-of-experts transformers for fleximodal fusion.Advances in Neural Information Processing Systems, 37: 67850–67900

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.920104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80482a20-6790-4c0f-a4b4-aff3aadda203 · outbound

This paper cites Mind the missing: Variable-aware representation learning for irregular ehr time series using large language models.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Mind the missing: Variable-aware representation learning for irregular ehr time series using large language models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.774552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 95954b66-34a5-44aa-ba1d-0d8c24d7cff4 · outbound

This paper cites Unleashing the power of pre-trained language models for irregularly sampled time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Unleashing the power of pre-trained language models for irregularly sampled time series

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.906266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 44e0327e-2123-416b-8c61-6dc4fedbe85d · outbound

This paper cites A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.736882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1d635c00-48ff-48b3-98e1-38081c8c0795 · outbound

This paper cites Recurrent neural networks for multivariate time series with missing values.Scientific reports, 8(1):6085.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Recurrent neural networks for multivariate time series with missing values.Scientific reports, 8(1):6085

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.878378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 740741db-3bdc-4e25-8180-158608059f03 · outbound

This paper cites Interpolation-prediction networks for irregularly sampled time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Interpolation-prediction networks for irregularly sampled time series

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.759836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 06aee193-69f1-4eba-a67e-353520e7504a · outbound

This paper cites Multi-time attention networks for irregularly sampled time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Multi-time attention networks for irregularly sampled time series

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.888718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 70a5fe9e-680a-47dd-b6ca-f32107aba98b · outbound

This paper cites Adaptive time encoding for irregular multivariate time-series classification.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Adaptive time encoding for irregular multivariate time-series classification

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.893477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ca660ad2-8cc6-4291-b13a-3869077a2ec6 · outbound

This paper cites Latent ordinary differential equations for irregularly-sampled time series.Advances in neural information processing systems, 32.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Latent ordinary differential equations for irregularly-sampled time series.Advances in neural information processing systems, 32

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.788346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5296475-d87f-4684-b53c-38416291c722 · outbound

This paper cites Gru-ode-bayes: Continuous modeling of sporadically-observed time series.Advances in neural information processing systems, 32.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Gru-ode-bayes: Continuous modeling of sporadically-observed time series.Advances in neural information processing systems, 32

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.764390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b0d73974-42be-4262-ac81-8e5568797396 · outbound

This paper cites Neural controlled differential equations for irregular time series.Advances in neural information processing systems, 33: 6696–6707.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Neural controlled differential equations for irregular time series.Advances in neural information processing systems, 33: 6696–6707

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.783939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3ceab295-7c35-4d0d-bf12-1d7f99f2e611 · outbound

This paper cites Modeling irregular time series with continuous recurrent units.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Modeling irregular time series with continuous recurrent units

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.768656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 79b77a46-b397-4aea-997e-c574a93a0571 · outbound

This paper cites Neural continuous-discrete state space models for irregularly-sampled time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Neural continuous-discrete state space models for irregularly-sampled time series

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:17.025702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4f0be5a6-1b95-4500-bf2a-526dd7668ebb · outbound

This paper cites Contiformer: Continuous-time transformer for irregular time series modeling.Advances in Neural Information Processing Systems, 36:47143–47175.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Contiformer: Continuous-time transformer for irregular time series modeling.Advances in Neural Information Processing Systems, 36:47143–47175

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.792686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0dae9fd4-c143-449e-aabc-06032880a00c · outbound

This paper cites Warpformer: A multi-scale modeling approach for irregular clinical time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Warpformer: A multi-scale modeling approach for irregular clinical time series

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.772552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea393754-f405-46fe-b0c8-62f0b29fc987 · outbound

This paper cites Set functions for time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Set functions for time series

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.902027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 95468ad1-4856-407c-8e68-f26bd52e6eba · outbound

This paper cites Graph-guided net- work for irregularly sampled multivariate time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Graph-guided net- work for irregularly sampled multivariate time series

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.898305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dd4d8157-e0ba-40a7-93f7-2134fe8498f2 · outbound

This paper cites Irregular multivariate time series forecasting: A transformable patching graph neural networks approach.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Irregular multivariate time series forecasting: A transformable patching graph neural networks approach

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.851121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a35b6d5b-73d1-4848-ac7f-df4029bfef41 · outbound

This paper cites Grafiti: Graphs for forecasting irregularly sampled time series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Grafiti: Graphs for forecasting irregularly sampled time series

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.864564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e20bd12d-b59d-4ef8-ab98-1626162c1ecd · outbound

This paper cites Time series as images: Vision transformer for irregularly sampled time series.Advances in Neural Information Processing Systems, 36:49187–49204.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Time series as images: Vision transformer for irregularly sampled time series.Advances in Neural Information Processing Systems, 36:49187–49204

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.846405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bfcd00dd-77b2-4dd3-a2c4-56a1f02a81de · outbound

This paper cites Promptcast: A new prompt-based learning paradigm for time series forecasting.IEEE Transactions on Knowledge and Data Engineering, 36(11):6851–6864.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Promptcast: A new prompt-based learning paradigm for time series forecasting.IEEE Transactions on Knowledge and Data Engineering, 36(11):6851–6864

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.869213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 392b127d-daf4-4d87-88a1-657dd2765109 · outbound

This paper cites Large language models are zero-shot time series forecasters.Advances in neural information processing systems, 36: 19622–19635.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Large language models are zero-shot time series forecasters.Advances in neural information processing systems, 36: 19622–19635

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.990391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ba5c7cc4-386d-4f40-be46-a72ebd7dab36 · outbound

This paper cites TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:19:27.819666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 247e3a53-be2a-4d2e-b1d7-906b4ff9dafd · outbound

This paper cites One fits all: Power general time series analysis by pretrained lm.Advances in neural information processing systems, 36:43322–43355.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling One fits all: Power general time series analysis by pretrained lm.Advances in neural information processing systems, 36:43322–43355

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.882628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da85ae4d-9eda-425c-b356-4debb1b15b26 · outbound

This paper cites Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.999101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 71f339d1-d383-42a7-a415-1532468f9829 · outbound

This paper cites TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.800250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 68bcbcda-3f24-402a-afeb-b68e66a06dc7 · outbound

This paper cites Autotimes: Au- toregressive time series forecasters via large language models.Advances in Neural Information Processing Systems, 37:122154–122184.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Autotimes: Au- toregressive time series forecasters via large language models.Advances in Neural Information Processing Systems, 37:122154–122184

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.937797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f41d8a7-851b-4c77-8c52-d3a864ad9529 · outbound

This paper cites S2ip-llm: Semantic space informed prompt learning with llm for time series forecasting.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling S2ip-llm: Semantic space informed prompt learning with llm for time series forecasting

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.819555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4316d812-aabc-438c-b864-ed6096d4cc46 · outbound

This paper cites Calf: Aligning llms for time series forecasting via cross-modal fine-tuning.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Calf: Aligning llms for time series forecasting via cross-modal fine-tuning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.933252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6ff63bc-15b7-4ff8-9a44-1b7988d3ff43 · outbound

This paper cites Timecma: Towards llm-empowered multivariate time series forecasting via cross-modality alignment.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Timecma: Towards llm-empowered multivariate time series forecasting via cross-modality alignment

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.873817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 483a97e1-1ad5-493c-b6db-f0e995689f95 · outbound

This paper cites Multimodal llms for health grounded in individual-specific data.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Multimodal llms for health grounded in individual-specific data

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.832783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 45692953-a40a-4a63-b6ac-0bb809302e9e · outbound

This paper cites MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.770192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f178e947-68c7-4655-a65e-4db4de6a4938 · outbound

This paper cites Gpt4mts: Prompt-based large language model for multimodal time-series forecasting.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Gpt4mts: Prompt-based large language model for multimodal time-series forecasting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.980449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2354cd7d-04eb-4a14-8e7c-d17022d51657 · outbound

This paper cites Instructime: Advancing time series classification with multimodal language modeling.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Instructime: Advancing time series classification with multimodal language modeling

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.947189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e4c50bee-ddcd-4ad6-996c-ca0b1de8cda6 · outbound

This paper cites Chattime: A unified multimodal time series foundation model bridging numerical and textual data.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Chattime: A unified multimodal time series foundation model bridging numerical and textual data

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.910938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2f0b7cc7-4baf-45aa-926c-b1255c75a90b · outbound

This paper cites Timecap: Learning to contextualize, augment, and predict time series events with large language model agents.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Timecap: Learning to contextualize, augment, and predict time series events with large language model agents

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.823717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5545db8-0a94-43c6-b39c-f203f48e8aea · outbound

This paper cites Patrick Langer, Thomas Kaar, Max Rosenblattl, Maxwell A.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Patrick Langer, Thomas Kaar, Max Rosenblattl, Maxwell A

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:19:27.750628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 742eb6e7-92b7-4026-9dff-05b1f1b62de7 · outbound

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

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling A decoder-only foundation model for time-series forecasting

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:07:21.325848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec1e423b-a006-4c8b-a7a9-557d9212def8 · outbound

This paper cites Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.807732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa428ca6-663e-471c-b624-3d45170af40c · outbound

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

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Chronos: Learning the Language of Time Series

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:19:27.756292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da929f20-90a1-4642-a532-ab8c49b1a3ee · outbound

This paper cites Chronos-2: From Univariate to Universal Forecasting.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Chronos-2: From Univariate to Universal Forecasting

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:20:36.981071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 63ea7706-cde6-4320-ba2e-c52fea5340d3 · outbound

This paper cites TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:19:27.813695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 82d61822-c177-4093-a58d-7fdaf3470c5f · outbound

This paper cites LAST SToP For Modeling Asynchronous Time Series.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling LAST SToP For Modeling Asynchronous Time Series

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.703452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 591da75e-91ea-4417-8617-f10420dc789f · outbound

This paper cites Byte-token enhanced language models for temporal point processes analysis.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Byte-token enhanced language models for temporal point processes analysis

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.951854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec42553b-3266-4935-ae8b-aec504512164 · outbound

This paper cites an unresolved cited work.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-05-15T16:21:16.924217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a3aca4c2-f7f3-4b55-ae71-63625257eeaf · outbound

This paper cites Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?Journal of biomedical informatics, 139:104306.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?Journal of biomedical informatics, 139:104306

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.975231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c421fc3b-292f-4fed-a2be-c0d16de6e3d7 · outbound

This paper cites John Wiley & Sons.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling John Wiley & Sons

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.969691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a32725f-064c-47dd-98c3-1534a08be53c · outbound

This paper cites Analysis of longitudinal data with irregular, outcome-dependent follow-up.Journal of the Royal Statistical Society Series B: Statistical Methodology, 66(3):791–813.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Analysis of longitudinal data with irregular, outcome-dependent follow-up.Journal of the Royal Statistical Society Series B: Statistical Methodology, 66(3):791–813

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.985444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1ec15c92-0111-42e8-8ff2-4997c02789fc · outbound

This paper cites Account- ing for informative sampling when learning to forecast treatment outcomes over time.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Account- ing for informative sampling when learning to forecast treatment outcomes over time

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.797038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 70ba2ea6-1f52-4e14-805c-9715ff4f56b6 · outbound

This paper cites Mixed-effects models for health care longitudinal data with an informative visiting process: A monte carlo simulation study.Statistica Neerlandica, 74(1):5–23.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Mixed-effects models for health care longitudinal data with an informative visiting process: A monte carlo simulation study.Statistica Neerlandica, 74(1):5–23

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.960783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 44adda14-9e6e-4e26-a0f2-793b1a5971a5 · outbound

This paper cites Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.792783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e96ed2a3-88d6-4223-ad3a-054ca6d6b4fb · outbound

This paper cites Mind the data gap: Missingness still shapes large language model prognoses.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Mind the data gap: Missingness still shapes large language model prognoses

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:27.762369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dbebc1aa-4284-4580-ac6e-124a714b42c6 · outbound

This paper cites MIMIC-IV Clinical Database Demo.PhysioNet, January 2023.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling MIMIC-IV Clinical Database Demo.PhysioNet, January 2023

Reference 62

Resolution: verified exact; doi, observed 2026-05-14T20:17:55.591804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a0302704-2dcd-46c9-bf0b-61ef094ae77a · outbound

This paper cites Alistair Johnson, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Alistair Johnson, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark

Reference 63

Resolution: metadata mismatch; doi, observed 2026-05-14T20:17:55.587956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4a5c912a-9935-48da-a430-66b49560c65b · outbound

This paper cites Leveraging large language models for multiple choice question answering.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Leveraging large language models for multiple choice question answering

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.860431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 95d15653-f4e7-4688-87f6-ab2215f6e79f · outbound

This paper cites an unresolved cited work.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-05-15T16:21:16.942008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bf2197ea-1dbc-4349-bf26-e53ddc92f67f · outbound

This paper cites Multitask learning and benchmarking with clinical time series data.Scientific data, 6(1):96.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Multitask learning and benchmarking with clinical time series data.Scientific data, 6(1):96

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.828383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e4f2c2c9-7ac1-4d17-9654-b5b29c3292e5 · outbound

This paper cites Benchmarking machine learning models on multi-centre eicu critical care dataset.Plos one, 15(7):e0235424.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Benchmarking machine learning models on multi-centre eicu critical care dataset.Plos one, 15(7):e0235424

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:17.003721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation de111535-65bc-40e6-ba38-08c1d9b3f35e · outbound

This paper cites Biobert: a pre-trained biomedical language representation model for biomedical text mining.Bioinformatics, 36(4):1234–1240.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Biobert: a pre-trained biomedical language representation model for biomedical text mining.Bioinformatics, 36(4):1234–1240

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:17.008566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bf4b18a7-c6e2-4bdc-8610-d403daeec038 · outbound

This paper cites Qwen3 Technical Report.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Qwen3 Technical Report

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:19:27.763856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 99801bd5-b34a-4222-9db1-391f71d1494d · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Qlora: Efficient finetuning of quantized llms.Advances in neural information processing systems, 36:10088– 10115

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.806663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9df6366d-721f-46fc-be80-52cbbdcf85a3 · outbound

This paper cites Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37: 62557–62583.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Sglang: Efficient execution of structured language model programs.Advances in neural information processing systems, 37: 62557–62583

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.855873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4bf45229-8f30-49e1-ac2c-1577d2ead5c4 · outbound

This paper cites Mind the performance gap: examining dataset shift during prospective validation.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Mind the performance gap: examining dataset shift during prospective validation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.811242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 355e6380-ef2a-40dc-91c9-b1e749d9eb03 · outbound

This paper cites Retain: An interpretable predictive model for healthcare using reverse time attention mechanism.Advances in neural information processing systems, 29.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Retain: An interpretable predictive model for healthcare using reverse time attention mechanism.Advances in neural information processing systems, 29

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.841788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 803d1c32-a23d-41ab-abf2-5f5a4ee06b23 · outbound

This paper cites Decoupled weight decay regularization.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Decoupled weight decay regularization

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.815442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea396e5c-f626-4424-a3fb-d2054b2a9ed5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Adam: A Method for Stochastic Optimization

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:19:27.741358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be5711ea-9ba0-4406-a463-53070e9c3665 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Language models are unsupervised multitask learners.OpenAI blog, 1(8):9

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.801616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5da60e9c-f40c-4d81-9d62-fafb6376d176 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.780295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3458a123-9a06-459a-a554-4b784a0e5903 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.776563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 270b507c-2971-4305-9d70-daa2f4bd375d · outbound

This paper cites Time2Vec: Learning a Vector Representation of Time.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Time2Vec: Learning a Vector Representation of Time

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:19:27.735615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eb5107d1-8ea7-4a53-bdcf-c27b4a7ee34f · outbound

This paper cites MedGemma Technical Report.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling MedGemma Technical Report

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-05-14T20:19:27.729699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bfa06cd9-43d4-4cd6-b80b-766585f1e912 · outbound

This paper cites an unresolved cited work.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-05-15T16:21:16.837196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 13909ba1-f2b3-4168-a779-bc0d61d38d10 · outbound

This paper cites These observations are shown as time-channel pairs.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling These observations are shown as time-channel pairs

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.994741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ebcd4ab5-6c1e-4fe5-af06-b8764caf3fb9 · outbound

This paper cites an unresolved cited work.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-05-15T16:21:16.955983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5cd4cf76-aa56-4ee6-a723-75aba21ab772 · outbound

This paper cites - LONG_STAY: ICU stay >= 96 hours or death.

MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling - LONG_STAY: ICU stay >= 96 hours or death

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T16:21:16.965402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers