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Paper Citation Record · LEDGER

Learning Deformable Body Interactions With Adaptive Spatial Tokenization

As of 15 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.13707.

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

pith.paper-citation-record.v1
2507.13707 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:23:48.301705Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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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

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation 29fd3fc4-7cc0-4971-b1f1-1982b8cc127e · outbound

This paper cites Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez- Gonzalez, Peter W.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez- Gonzalez, Peter W

Reference 1

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Observation 63c7a405-c101-43b7-8cce-ce7f8fb1bb3a · outbound

This paper cites CRC press, 2023.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization CRC press, 2023

Reference 2

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Observation 725cdcfd-813b-4ca4-a195-bef20489e8b5 · outbound

This paper cites Efficient learning of mesh-based physical simulation with bi-stride multi-scale graph neural network.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Efficient learning of mesh-based physical simulation with bi-stride multi-scale graph neural network

Reference 3

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Source-reported events for the cited work

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Observation 9f7e4dbe-e93e-4f9f-a602-54498018bde6 · outbound

This paper cites A review of physics simulators for robotic applications.IEEE Access, 9:51416–51431, 2021.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization A review of physics simulators for robotic applications.IEEE Access, 9:51416–51431, 2021

Reference 4

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation cf8bbc08-9ec0-4da7-8b1b-ed92c0b2b6ac · outbound

This paper cites Variational methods for the solution of problems of equilibrium and vibrations.Lecture notes in pure and applied mathematics, pages 1–1, 1994.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Variational methods for the solution of problems of equilibrium and vibrations.Lecture notes in pure and applied mathematics, pages 1–1, 1994

Reference 5

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Observation 120180d2-7b64-4f7b-855f-68c09d85b7fd · outbound

This paper cites Application of the finite element method in the analysis of composite materials: A review.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Application of the finite element method in the analysis of composite materials: A review

Reference 6

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Observation 716aa891-8737-4233-b062-a43c6a4e1723 · outbound

This paper cites Bordas, and Jakub Lengiewicz.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Bordas, and Jakub Lengiewicz

Reference 7

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c4ad12d4-5257-4792-9e07-2878d1c48ac5 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 5bb2c820-6d7c-4ce9-be19-10811e438b98 · outbound

This paper cites Multiscale MeshGraphNets.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Multiscale MeshGraphNets

Reference 9

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Observation d213b464-154a-4d9f-98cf-9f165f382c5c · outbound

This paper cites GNN-based physics solver for time-independent PDEs.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization GNN-based physics solver for time-independent PDEs

Reference 10

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Observation 46bd4564-442e-4bf6-b2d9-8dbd831cdd37 · outbound

This paper cites Sparse 3d convolutional neural networks, 2015.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Sparse 3d convolutional neural networks, 2015

Reference 11

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Observation dfc45315-4250-4efe-a8ca-688090cd71e4 · outbound

This paper cites A robust workflow for b-rep generation from image masks.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization A robust workflow for b-rep generation from image masks

Reference 12

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Observation 5fd61722-c8be-42fb-9f4c-fb76a054f91e · outbound

This paper cites Abc: A big cad model dataset for geometric deep learning.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Abc: A big cad model dataset for geometric deep learning

Reference 13

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Observation d85e9611-e6d6-4d24-8dd2-3b09f1094b51 · outbound

This paper cites Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023

Reference 14

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Observation f0bf12d2-a59e-49c9-bfcb-f3a883c832a4 · outbound

This paper cites Mage: Masked Generative Encoder to Unify Representation Learning and Image Synthesis.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Mage: Masked Generative Encoder to Unify Representation Learning and Image Synthesis

Reference 15

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Observation 5d42c7b5-41e4-45f7-8167-c908ef397413 · outbound

This paper cites TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models

Reference 16

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Observation 710db2f0-2735-49ef-b18c-e21a31747fb0 · outbound

This paper cites Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks

Reference 17

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Observation 2191ba42-3d90-48d0-ba73-2f0b641f1b67 · outbound

This paper cites Geometric modeling using octree encoding.Computer graphics and image processing, 19(2):129–147, 1982.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Geometric modeling using octree encoding.Computer graphics and image processing, 19(2):129–147, 1982

Reference 18

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Observation 4c91c1a9-4fe2-48e7-bcce-2f40c23b6325 · outbound

This paper cites Neural Discrete Representation Learning.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Neural Discrete Representation Learning

Reference 19

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Source-reported events for the cited work

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Observation 0863d141-5915-4cf4-9a24-26c4d4ccd18e · outbound

This paper cites Learning mesh- based simulation with graph networks.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Learning mesh- based simulation with graph networks

Reference 20

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This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017

Reference 21

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Observation d567836e-2342-4b45-962c-b6ddbde2a854 · outbound

This paper cites Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and I.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and I

Reference 22

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Observation 7a3da1b8-d68b-4731-9a24-c780d70c4b00 · outbound

This paper cites Learning rigid-body simulators over implicit shapes for large-scale scenes and vision.Advances in Neural Information Processing Systems, 37:125809–125838, 2024.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Learning rigid-body simulators over implicit shapes for large-scale scenes and vision.Advances in Neural Information Processing Systems, 37:125809–125838, 2024

Reference 23

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Observation 5ad3f65d-1acc-40ff-89c0-536073d353bd · outbound

This paper cites Battaglia.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Battaglia

Reference 24

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Observation 1550478e-0baf-4512-ba90-899ac97d5427 · outbound

This paper cites GLU Variants Improve Transformer.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization GLU Variants Improve Transformer

Reference 25

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Observation ed9a7f69-403e-4450-8d82-bb38fc089988 · outbound

This paper cites A survey of finite element techniques for mechanism design.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization A survey of finite element techniques for mechanism design

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation cf3cf9e2-7c3e-48ef-bc01-7dbb010c7b7d · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 27

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Observation 5580baac-de7b-4012-be3a-8d821a26e7f3 · outbound

This paper cites Octformer: Octree-based Transformers for 3d Point Clouds.ACM Transac- tions on Graphics, 42(4):1–11, 2023.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Octformer: Octree-based Transformers for 3d Point Clouds.ACM Transac- tions on Graphics, 42(4):1–11, 2023

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 272b688a-3472-4acb-b45f-9ec643511f23 · outbound

This paper cites O-cnn: Octree-based convolutional neural networks for 3d shape analysis.ACM Transactions On Graphics (TOG), 36(4):1–11, 2017.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization O-cnn: Octree-based convolutional neural networks for 3d shape analysis.ACM Transactions On Graphics (TOG), 36(4):1–11, 2017

Reference 29

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b15b8182-9c4f-494a-98d1-3aee51ccad1b · outbound

This paper cites Random Weight Factorization Improves the Training of Continuous Neural Representations.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 30

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Observation 581fcbcd-d6f3-4940-91a6-bc3b55993192 · outbound

This paper cites Second: Sparsely embedded convolutional detection.Sensors, 18(10), 2018.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Second: Sparsely embedded convolutional detection.Sensors, 18(10), 2018

Reference 31

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Observation 926304de-2e80-4cf2-9a5a-6cdbbeb06f75 · outbound

This paper cites Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer

Reference 32

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Observation 23af31ae-f011-40e6-9cf7-dc2b6f457992 · outbound

This paper cites 3dshape2vecset: A 3d Shape Representation for Neural Fields and Generative Diffusion Models.ACM Transactions on Graphics (TOG), 42(4):92:1–92:16, 2023.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization 3dshape2vecset: A 3d Shape Representation for Neural Fields and Generative Diffusion Models.ACM Transactions on Graphics (TOG), 42(4):92:1–92:16, 2023

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c2dcdb65-0285-4e8a-af39-e68e6bff36aa · outbound

This paper cites 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models.ACM Transactions On Graphics (TOG), 42(4):1–16, 2023.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models.ACM Transactions On Graphics (TOG), 42(4):1–16, 2023

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T16:23:48.183346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:23:48.183346Z digest=sha256:6004be545145817253cfdc455abcee05df3db98bbfaa26aee08e3b3525d8e8a9

Observation 5f92b26f-13e0-4153-8aef-0f5620135ee4 · outbound

This paper cites Elsevier, 2005.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Elsevier, 2005

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:23:48.597892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:23:48.233005Z digest=sha256:1457288829840177255145f75cad868cf6761fdd00757bba3bd5c970ce102954

Observation 11589b6b-9f6d-4edb-81b6-39b9e74b3234 · outbound

This paper cites We note that in the BSMS paper, the authors included velocity as an input feature for DEFORMINGPLATE.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization We note that in the BSMS paper, the authors included velocity as an input feature for DEFORMINGPLATE

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:23:48.448850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-06T16:23:48.301705Z digest=sha256:2b12f4bf8f5b5f61dd12434c62648588006491eeca18dd20b5ff955a06d4c1f4

Pith citing papers

No inbound Pith citation observations are available.