Paper Citation Record · LEDGER

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density

As of 7 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2608.03260.

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

pith.paper-citation-record.v1
2608.03260 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:26:38.475551Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

22 of 22 outbound references displayed

verified exact5
verified fuzzy1
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2fa0c340-1ba6-4b0a-bc97-eaa4484bedbd · outbound

This paper cites ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.418381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb5ae5b5-6c5c-4131-97d0-c406f785a543 · outbound

This paper cites Self-Supervised Graph Transformer on Large-Scale Molecular Data.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Self-Supervised Graph Transformer on Large-Scale Molecular Data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.422153Z

Source-reported events for the cited work

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Observation 8e1fa422-9050-4713-821d-1be7b58923ef · outbound

This paper cites Molecular Contrastive Learning of Representations via Graph Neural Networks.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Molecular Contrastive Learning of Representations via Graph Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.425760Z

Source-reported events for the cited work

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Observation 02c1ed9d-9d10-40a8-ba68-0b83a1a8ff91 · outbound

This paper cites Pre-training Molecular Graph Representation with 3D Geometry.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Pre-training Molecular Graph Representation with 3D Geometry

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.429688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a69412e-949a-45f9-a155-4da6489b52ba · outbound

This paper cites 3D Infomax improves GNNs for Molecular Property Prediction.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density 3D Infomax improves GNNs for Molecular Property Prediction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.433263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d71b0ad-5c4d-4880-a963-df256a391f50 · outbound

This paper cites Unified 2D and 3D Pre-Training of Molecular Representations.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Unified 2D and 3D Pre-Training of Molecular Representations

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:26:38.699541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 654d3bef-425d-4e80-8685-43225e20624e · outbound

This paper cites Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.440900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 501aa2ac-a9be-4364-860d-1961dc2e324a · outbound

This paper cites 3d-molt5: leveraging discrete structural information for molecule-text modeling.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density 3d-molt5: leveraging discrete structural information for molecule-text modeling

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:26:38.873593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 153636bb-9e62-4ac7-be9d-b0afad072dd9 · outbound

This paper cites Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT).

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.452784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f719cd16-2c6e-44c7-92f3-ab91072c3a59 · outbound

This paper cites Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:26:38.564806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9eb698ac-7bf4-4e94-85e2-30afbe705f82 · outbound

This paper cites Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:26:38.549759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b7941a2-d4bc-47be-a651-e8a0a5de6c00 · outbound

This paper cites Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T22:26:38.522580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b588965d-698a-4a3f-badd-c266e50b97e6 · outbound

This paper cites Masked Autoencoders for Point Cloud Self-supervised Learning.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Masked Autoencoders for Point Cloud Self-supervised Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.475551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0c0b1e0-65b8-49a0-a854-346aac9abd68 · outbound

This paper cites Edbench: Large-scale electron density data for molecular modeling.arXiv preprint arXiv:2505.09262,.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Edbench: Large-scale electron density data for molecular modeling.arXiv preprint arXiv:2505.09262,

Reference 1964

Resolution: verified exact; raw_fallback, observed 2026-08-05T22:26:38.860537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97ac1686-9422-4cc4-ba70-6af049493589 · outbound

This paper cites Directional Message Passing for Molecular Graphs.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Directional Message Passing for Molecular Graphs

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.406823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3d75feb-0c9d-4d83-a855-9b0eb7de592d · outbound

This paper cites SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.402961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2cb219e-1354-4be1-aefb-7d96438bce16 · outbound

This paper cites Equivariant message passing for the prediction of tensorial properties and molecular spectra.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Equivariant message passing for the prediction of tensorial properties and molecular spectra

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.410943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 447d795a-f109-416c-83b3-375b460ab5b5 · outbound

This paper cites GemNet: Universal Directional Graph Neural Networks for Molecules.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density GemNet: Universal Directional Graph Neural Networks for Molecules

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.414689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75e2ae4e-b8fd-4841-a105-d6792f6f62f2 · outbound

This paper cites UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.398883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df79069c-8c4f-47cb-8e79-3c5a53f7042b · outbound

This paper cites PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.468121Z

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Observation 0161b450-4da6-4ec7-bf5a-c1587ea0ce1a · outbound

This paper cites Scireasoner: Laying the scientific reasoning ground across disciplines.arXiv preprint arXiv:2509.21320,.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density Scireasoner: Laying the scientific reasoning ground across disciplines.arXiv preprint arXiv:2509.21320,

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.449116Z

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Unavailable: canonical work link unavailable.

Observation e066f243-3f8d-4a95-a33f-b098db05dc23 · outbound

This paper cites DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.456534Z

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Pith citing papers

No inbound Pith citation observations are available.