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Paper Citation Record · LEDGER

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration

As of 23 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2608.08689.

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

pith.paper-citation-record.v1
2608.08689 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:33:36.094446Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5aeee118-90f1-49a4-a733-d258ef637649 · outbound

This paper cites World Models.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration World Models

Reference 1

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Observation 8be5ca5a-49a2-478b-961e-840d57604078 · outbound

This paper cites Learning Latent Dynamics for Planning from Pixels.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Learning Latent Dynamics for Planning from Pixels

Reference 2

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Observation 87af7403-f33e-4d36-b0c4-f506f1e38f80 · outbound

This paper cites Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray Kavukcuoglu.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray Kavukcuoglu

Reference 3

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Observation 38772879-b796-494a-9db5-1866ec8fa964 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Relational inductive biases, deep learning, and graph networks

Reference 4

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Observation 6168aa98-87c1-44f0-90b2-cd5a83edff62 · outbound

This paper cites Schoenholz, Patrick F.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Schoenholz, Patrick F

Reference 5

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Observation bb763e55-dbd4-4440-970e-9a4eee7a67cd · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Semi-Supervised Classification with Graph Convolutional Networks

Reference 6

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Observation 1cc2b391-ccdd-4ab4-893e-df69412c72a2 · outbound

This paper cites Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4470–4479, 2018.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4470–4479, 2018

Reference 7

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Observation 4768d4c7-9afd-4b2d-954c-fdf86135ca79 · outbound

This paper cites Learning to Simulate Complex Physics with Graph Networks.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Learning to Simulate Complex Physics with Graph Networks

Reference 8

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Observation 39318514-8cb0-4d2e-b23f-346e7e54a648 · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 9

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Source-reported events for the cited work

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Observation 3228621e-4e0e-4c0d-88c9-de3f40e5888f · outbound

This paper cites Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints

Reference 10

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Observation 08deb0cf-d012-4896-b7fb-e284dc50c752 · outbound

This paper cites Forrester.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Forrester

Reference 11

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Observation d2a37f48-c916-49de-b646-e157f8e2b0e2 · outbound

This paper cites Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Reference 12

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This paper cites Graph World Model.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Graph World Model

Reference 13

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Observation 69508b2e-2560-4700-9dcf-08c2384a38ba · outbound

This paper cites Graph World Models: Concepts, Taxonomy, and Future Directions.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Graph World Models: Concepts, Taxonomy, and Future Directions

Reference 14

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Observation 0ea982aa-ae7a-4b53-8432-790ca92eed33 · outbound

This paper cites Understanding Rollout Error in Graph World Models.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Understanding Rollout Error in Graph World Models

Reference 15

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Source-reported events for the cited work

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Observation cdd756c6-4dc7-4349-8faf-83b1c6589660 · outbound

This paper cites Learning Physical Constraints with Neural Projec- tions.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Learning Physical Constraints with Neural Projec- tions

Reference 16

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Source-reported events for the cited work

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Observation e713e259-1486-4b26-aea1-6067ea618538 · outbound

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

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 17

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This paper cites Long Short-Term Memory.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Long Short-Term Memory

Reference 18

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This paper cites T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

Reference 19

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This paper cites Physics-informed neural net- works: A deep learning framework for solving forward and inverse problems involving non- linear partial differential equations.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Physics-informed neural net- works: A deep learning framework for solving forward and inverse problems involving non- linear partial differential equations

Reference 20

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This paper cites Lagrangian Neural Networks.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Lagrangian Neural Networks

Reference 21

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This paper cites GNNEx- plainer: Generating Explanations for Graph Neural Networks.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration GNNEx- plainer: Generating Explanations for Graph Neural Networks

Reference 22

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Source-reported events for the cited work

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Observation a845b15c-1b52-4b0e-9fd4-4c3f2f38166a · outbound

This paper cites Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

Reference 23

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This paper cites Vrachimis, Demetrios G.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Vrachimis, Demetrios G

Reference 24

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A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Unresolved cited work

Reference 25

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Observation a24f5df5-dd9c-4283-a316-feaea8eee9ee · outbound

This paper cites Birchfield, Ti Xu, Kathleen M.

A Structural Dynamics Graph World Model: Unified Modeling, Constrained Rollout, and Interpretable Calibration Birchfield, Ti Xu, Kathleen M

Reference 26

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