Paper Citation Record · LEDGER

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting

As of 18 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 4 inbound Pith citation observations for arXiv:2505.12738.

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

pith.paper-citation-record.v1
2505.12738 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:32:50.593657Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T07:22:25.349398Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T09:06:49.121521Z

Reference resolution

49 of 49 outbound references displayed

verified exact3
verified fuzzy25
unresolved20
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b28340a-7bc9-4680-8ba0-1bf9e8869e23 · outbound

This paper cites The covid-19 pandemic.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting The covid-19 pandemic

Reference 1

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Source-reported events for the cited work

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Observation c6220515-44e9-4032-91c6-a0345a349d3d · outbound

This paper cites Covid- 19 epidemic prediction and the impact of public health interventions: A review of covid-19 epidemic models.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Covid- 19 epidemic prediction and the impact of public health interventions: A review of covid-19 epidemic models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.117675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 72683625-d449-45f4-bd95-7e4c330d013e · outbound

This paper cites An enhanced seir model for prediction of covid-19 with vaccination effect.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting An enhanced seir model for prediction of covid-19 with vaccination effect

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.108029Z

Source-reported events for the cited work

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Observation 36ff80ec-2314-4ea1-bf37-65d02ee9988c · outbound

This paper cites Fitting and forecasting the trend of covid-19 by seir (+ caq) dynamic model.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Fitting and forecasting the trend of covid-19 by seir (+ caq) dynamic model

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.097677Z

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Observation 70418c52-936e-4195-8690-ff2f0cd83ef6 · outbound

This paper cites Mathematical modelling to inform new zealand’s covid-19 response.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Mathematical modelling to inform new zealand’s covid-19 response

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.087383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f4f0f321-276b-40bd-90b5-48d6c2c68509 · outbound

This paper cites Bangladesh covid-19 daily cases time series analysis using facebook prophet model.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Bangladesh covid-19 daily cases time series analysis using facebook prophet model

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.076885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a6d0d687-2ae5-48b1-98f6-81c40ef87105 · outbound

This paper cites Arima-based forecasting of the dynamics of confirmed covid-19 cases for selected european countries.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Arima-based forecasting of the dynamics of confirmed covid-19 cases for selected european countries

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.065301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aca3c6d9-fb57-4c5d-a13b-dcb7fa2fa7b9 · outbound

This paper cites Why is it difficult to accurately predict the covid-19 epidemic? Infectious disease modelling, 5:271–281, 2020.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Why is it difficult to accurately predict the covid-19 epidemic? Infectious disease modelling, 5:271–281, 2020

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.054519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cdd1641e-ed4a-4bf6-8da2-dda74d51b45f · outbound

This paper cites Deep learning for epidemiolog- ical predictions.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Deep learning for epidemiolog- ical predictions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.043133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a50f68ae-2291-423c-a6bf-12f78eab3bda · outbound

This paper cites Prediction of epidemic trends in covid-19 with logistic model and machine learning technics.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Prediction of epidemic trends in covid-19 with logistic model and machine learning technics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.031756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6d6517de-5e42-4e8b-a67d-732cc014b964 · outbound

This paper cites Forecasting prediction of covid-19 outbreak using linear regression.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Forecasting prediction of covid-19 outbreak using linear regression

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.020691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 87bc7e60-8357-4638-b473-bfe4bb6467b3 · outbound

This paper cites A random forest model for forecasting regional covid-19 cases utilizing reproduction number estimates and demographic data.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting A random forest model for forecasting regional covid-19 cases utilizing reproduction number estimates and demographic data

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:51.008934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3289cc08-66e9-4d31-b826-fbaae72c4c8e · outbound

This paper cites Application of a data- driven xgboost model for the prediction of covid-19 in the usa: a time-series study.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Application of a data- driven xgboost model for the prediction of covid-19 in the usa: a time-series study

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.998118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 62c1874e-fde5-4f50-8a75-89292ca478a0 · outbound

This paper cites Time series forecasting of covid-19 transmission in canada using lstm networks.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Time series forecasting of covid-19 transmission in canada using lstm networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.987713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 37096a9b-98f0-4a1a-9a2e-abd6b47a4c1f · outbound

This paper cites Transfer graph neural networks for pandemic forecasting.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Transfer graph neural networks for pandemic forecasting

Reference 15

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Source-reported events for the cited work

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Observation 35fa339b-cb99-4dff-968d-fa134274d4b8 · outbound

This paper cites Spatio-Temporal Graph Neural Networks: A Survey.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Spatio-Temporal Graph Neural Networks: A Survey

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.479878Z

Source-reported events for the cited work

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Observation b2f27b96-5921-4b1d-8503-3e73a4a01eb6 · outbound

This paper cites Spatio-temporal graph learning for epidemic prediction.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Spatio-temporal graph learning for epidemic prediction

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.972066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 02616ce7-749f-4bbe-ab69-a5891b3424f2 · outbound

This paper cites A review of graph neural networks in epidemic modeling.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting A review of graph neural networks in epidemic modeling

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.961530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f98dc62-dcd2-473d-a920-b950e071a1ec · outbound

This paper cites Revolutionizing Finance with LLMs: An Overview of Applications and Insights.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Revolutionizing Finance with LLMs: An Overview of Applications and Insights

Reference 19

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Source-reported events for the cited work

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Observation af071fea-961e-4abf-9db5-5b0711210721 · outbound

This paper cites AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.494083Z

Source-reported events for the cited work

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Observation dcf4f063-d14d-4db7-9516-d534f7c9e7eb · outbound

This paper cites Llm multimodal traffic accident forecasting.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Llm multimodal traffic accident forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.951829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9cb45ed9-83bd-4f89-8254-eb030a939804 · outbound

This paper cites Autotimes: Au- toregressive time series forecasters via large language models.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Autotimes: Au- toregressive time series forecasters via large language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.942039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0a625afe-1573-4613-9bbb-1e86bc756c4c · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.504467Z

Source-reported events for the cited work

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Observation 8f4e8755-6321-468d-89ac-9c7c74f0c574 · outbound

This paper cites Real-time epidemic forecasting: challenges and opportunities.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Real-time epidemic forecasting: challenges and opportunities

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.932376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff8aff99-ce81-42b2-b2b0-3578b5c27122 · outbound

This paper cites Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:32:50.794741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b07364ae-2ad9-43f6-8e01-d9ad4c8d93b3 · outbound

This paper cites When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.514821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 181aaf48-1676-468c-9761-5128ec4f636d · outbound

This paper cites Temporal multires- olution graph neural networks for epidemic prediction.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Temporal multires- olution graph neural networks for epidemic prediction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.922532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 990cb56a-6cd1-43f2-8b77-9cacf3572176 · outbound

This paper cites What language model architecture and pretraining objective works best for zero-shot generalization? In International Conference on Machine Learning, pages 22964–22984.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting What language model architecture and pretraining objective works best for zero-shot generalization? In International Conference on Machine Learning, pages 22964–22984

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.913722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 873bc8a0-b060-4c7e-968f-36d9a1c12f6b · outbound

This paper cites An Empirical Study of Autoregressive Pre-training from Videos.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting An Empirical Study of Autoregressive Pre-training from Videos

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.525435Z

Source-reported events for the cited work

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Observation b3f14036-ce82-46b5-a1dc-becdb995be2f · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.528790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fc042da-10ec-4ef4-aa3d-e16ccf15d1ef · outbound

This paper cites Graph Prompt Learning: A Comprehensive Survey and Beyond.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Graph Prompt Learning: A Comprehensive Survey and Beyond

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.531705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eac10862-a8aa-407f-9fdc-6b952b04f778 · outbound

This paper cites A survey of time series foundation models: Generalizing time series representation with large language model.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting A survey of time series foundation models: Generalizing time series representation with large language model

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.534905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e6721b4-ecb3-4869-a3ec-aa96df06f880 · outbound

This paper cites Enhanced gaussian process regression-based forecasting model for covid-19 outbreak and significance of iot for its detection.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Enhanced gaussian process regression-based forecasting model for covid-19 outbreak and significance of iot for its detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.898351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 85b8098f-9238-4338-bb04-c50c88dffe63 · outbound

This paper cites Exploring graph structure in graph neural networks for epidemic forecasting.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Exploring graph structure in graph neural networks for epidemic forecasting

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.888189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eaaa07b5-1ea3-42ec-b409-360a9490063e · outbound

This paper cites Attention is all you need.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Attention is all you need

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.545877Z

Source-reported events for the cited work

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Observation 45c868c8-4ef1-45f4-af6c-5c02d30bf2de · outbound

This paper cites Multiresolution equivariant graph variational autoencoder.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Multiresolution equivariant graph variational autoencoder

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.549019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b153042-5447-4ee0-8421-eca878c43bc7 · outbound

This paper cites Foundation models for time series analysis: A tutorial and survey.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Foundation models for time series analysis: A tutorial and survey

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.552114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6374dc72-9b5b-45cf-b134-032b110f7811 · outbound

This paper cites Agents with foundation models: advance and vision.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Agents with foundation models: advance and vision

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.862345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 83bd262b-4070-49a0-8bbd-96839a1477c6 · outbound

This paper cites Infectious Disease Forecasting in India using LLM's and Deep Learning.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Infectious Disease Forecasting in India using LLM's and Deep Learning

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:32:50.695429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb92f45a-5c3d-4c9f-87cf-b1615b9ee0e3 · outbound

This paper cites Do we really need foundation models for multi-step-ahead epidemic forecasting? In NeurIPS Workshop on Time Series in the Age of Large Models, 2024.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Do we really need foundation models for multi-step-ahead epidemic forecasting? In NeurIPS Workshop on Time Series in the Age of Large Models, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:32:50.852446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7a815148-c95b-49a5-b3d0-bfe8b22c4b38 · outbound

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

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Chronos: Learning the Language of Time Series

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.566354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a1e74ec-071a-42cf-9f66-6e6c5f4a1f1a · outbound

This paper cites Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.569814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 958e27d0-d768-40de-a098-cd4d8c846e09 · outbound

This paper cites Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:32:50.665869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b014e0e7-1223-4325-b753-9164cca07a4b · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.577026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cf5cd52-290a-4c4b-85ba-99b8d0d0b4e9 · outbound

This paper cites Language models are unsupervised multitask learners.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Language models are unsupervised multitask learners

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.580542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33f9c9a3-8055-41e1-a934-a3879d0772fe · outbound

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

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.583886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5218223-073c-4678-8eed-283fccbde8c8 · outbound

This paper cites Gemma 3 Technical Report.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Gemma 3 Technical Report

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.586994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 016fedd8-5e9a-42fa-8e23-c34ceadf048e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Adam: A Method for Stochastic Optimization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T20:32:50.590481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a43b6f40-7645-4b4f-9a05-4bc0f874b6cd · outbound

This paper cites Neural message passing for quantum chemistry.

EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting Neural message passing for quantum chemistry

Reference 49

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T20:32:50.837168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 74f3a6bf-9248-4e03-8cf5-dc5d388d2350 · inbound

EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting cites this paper.

EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:42:03.861284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4123dc3b-3755-459c-ad3b-dba8811839b2 · inbound

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches cites this paper.

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting

Reference 95

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:56:14.423521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b45bd4a-4f2a-45ba-9344-5ad6cb030d21 · inbound

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches cites this paper.

Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:35:33.991252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 853aa053-9632-4be1-9ffe-fd519659b02f · inbound

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts cites this paper.

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T09:06:49.123030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.