Paper Citation Record · LEDGER

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

As of 2 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2605.02699.

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

pith.paper-citation-record.v1
2605.02699 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T18:13:10.511893Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-02T06:30:47.504484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T09:46:45.685374Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy35
unresolved3
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8914dcb0-1d4f-4525-b836-4f5103670d1a · outbound

This paper cites A review of learning-based dynam- ics models for robotic manipulation.Science Robotics, 10(106):eadt1497.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions A review of learning-based dynam- ics models for robotic manipulation.Science Robotics, 10(106):eadt1497

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.381680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 50aa009b-c187-4be1-b76e-bc0bc00f62a1 · outbound

This paper cites Combining physical simulators and object- based networks for control.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Combining physical simulators and object- based networks for control

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.290326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 574bc059-82d9-4b5b-a547-ef7e586d21cd · outbound

This paper cites an unresolved cited work.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-05-26T05:36:45.375050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 3dab8e3d-9e81-4d1d-8854-f3bcb7349693 · outbound

This paper cites Hesselink, Elise van der Pol, Erik J.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Hesselink, Elise van der Pol, Erik J

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.294498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 9a561eaa-617f-4092-a6a8-6549b3d55fa5 · outbound

This paper cites Daxbench: Benchmarking deformable object manipulation with dif- ferentiable physics.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Daxbench: Benchmarking deformable object manipulation with dif- ferentiable physics

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.281668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation c92ae6a0-6a3b-43f6-bd81-dcc6430abac8 · outbound

This paper cites The cross-entropy method: A uni- fied approach to combinatorial optimization, monte-carlo simulation, and machine learning.Technometrics, 48(1): 147–148.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions The cross-entropy method: A uni- fied approach to combinatorial optimization, monte-carlo simulation, and machine learning.Technometrics, 48(1): 147–148

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.298634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 9b349409-a5b3-4b2a-8e7e-cfabbe7c0e3c · outbound

This paper cites Sim-to-real of soft robots with learned residual physics.IEEE Robotics and Automation Letters.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Sim-to-real of soft robots with learned residual physics.IEEE Robotics and Automation Letters

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.326009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 4a03e598-6adf-4874-8073-8e872810e3db · outbound

This paper cites Learning latent dynamics for planning from pixels.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Learning latent dynamics for planning from pixels

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.356549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 30be3779-50b3-4e3f-8bd7-5f1caaef5d1e · outbound

This paper cites an unresolved cited work.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-26T05:36:45.343133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation a74f6b6d-d5ed-4d8f-beac-832fe6338431 · outbound

This paper cites Mastering diverse domains through world models.Nature.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Mastering diverse domains through world models.Nature

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.349639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation e0975a5b-b0cc-4bb5-bde1-1115822573f9 · outbound

This paper cites Learning physical dynamics with subequivariant graph neural networks.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Learning physical dynamics with subequivariant graph neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.305518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 7af3d22e-8f67-4c3c-9d55-a20c7c4f5723 · outbound

This paper cites Mesh-based dynamics with occlusion reasoning for cloth manipula- tion.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Mesh-based dynamics with occlusion reasoning for cloth manipula- tion

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.352855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation a4323a4b-2903-43c4-ba7d-5aa94c37a3a2 · outbound

This paper cites gradsim: Differen- tiable simulation for system identification and visuo- motor control.International Conference on Learning Representations (ICLR).

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions gradsim: Differen- tiable simulation for system identification and visuo- motor control.International Conference on Learning Representations (ICLR)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.360114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 9bb64cbb-36d4-4fc2-9b74-d92d99955b8d · outbound

This paper cites PhysTwin: Physics- informed reconstruction and simulation of deformable objects from videos.ICCV.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions PhysTwin: Physics- informed reconstruction and simulation of deformable objects from videos.ICCV

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.273712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation f3ed22d2-80a2-46c2-a4e2-1b0380135412 · outbound

This paper cites 3D gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4), July 2023.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions 3D gaussian splatting for real-time radiance field rendering.ACM Transactions on Graphics, 42(4), July 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.285771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation dbf7f56e-78c1-47cc-831d-6251a1ac46df · outbound

This paper cites Se (2)-equivariant pushing dynamics models for tabletop object manipulations.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Se (2)-equivariant pushing dynamics models for tabletop object manipulations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.329219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 019445f4-6e40-41cb-b5e1-465da9d208bd · outbound

This paper cites Segment Anything.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Segment Anything

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T06:14:21.613788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 6aec9a55-e992-4c29-84fd-90b993fa0278 · outbound

This paper cites Context-aware dynamics model for generalization in model-based reinforcement learning.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Context-aware dynamics model for generalization in model-based reinforcement learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.335850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 58386a42-1032-421a-a4a3-d73911338a2c · outbound

This paper cites Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.277685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 708d4672-9ab7-4992-b390-4db8003d2271 · outbound

This paper cites Propagation networks for model-based control under partial observa- tion.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Propagation networks for model-based control under partial observa- tion

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.366291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation dd2663d2-b811-4384-b2ff-be2fc7f1f36c · outbound

This paper cites an unresolved cited work.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-26T05:36:45.269580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation ddd96d65-4c72-42dd-8270-34361ba73f49 · outbound

This paper cites Soft- MAC: Differentiable soft body simulation with forecast- based contact model and two-way coupling with articu- lated rigid bodies and clothes.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Soft- MAC: Differentiable soft body simulation with forecast- based contact model and two-way coupling with articu- lated rigid bodies and clothes

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.322749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation b4b206d5-ca7d-4bb5-b34c-e81ecc71a2ba · outbound

This paper cites Warp: A high-performance python frame- work for gpu simulation and graphics, March 2022.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Warp: A high-performance python frame- work for gpu simulation and graphics, March 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.264904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation d0d50f48-42dd-4dba-b07b-c545c95d7a95 · outbound

This paper cites Focused adaptation of dynamics mod- els for deformable object manipulation.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Focused adaptation of dynamics mod- els for deformable object manipulation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.339220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation cfec4e31-7c15-472d-9b86-96bf01031dcc · outbound

This paper cites Hierarchical foresight: Self- supervised learning of long-horizon tasks via visual sub- goal generation.International Conference on Learning Representations (ICLR).

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Hierarchical foresight: Self- supervised learning of long-horizon tasks via visual sub- goal generation.International Conference on Learning Representations (ICLR)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.319336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 20a9944c-c734-4cc2-a498-2f47c792536e · outbound

This paper cites Learning to simulate complex physics with graph net- works.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Learning to simulate complex physics with graph net- works

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.308657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 786a5fab-8f2b-472b-ab93-7b2a849d1548 · outbound

This paper cites E (n) equivariant graph neural networks.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions E (n) equivariant graph neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.332445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation a3186d87-16cf-4f95-9b76-f00b79296a73 · outbound

This paper cites The graph neural network model.IEEE transactions on neural networks, 20(1):61–80.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions The graph neural network model.IEEE transactions on neural networks, 20(1):61–80

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.315888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation ec81e81d-99e0-4f0f-ba38-207202bc7854 · outbound

This paper cites Pugs: Zero-shot physi- cal understanding with gaussian splatting.2025 IEEE International Conference on Robotics and Automation (ICRA).

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Pugs: Zero-shot physi- cal understanding with gaussian splatting.2025 IEEE International Conference on Robotics and Automation (ICRA)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.311944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 0b328e0a-ff05-41d7-bb13-7db1fdb509fb · outbound

This paper cites MIT press Cam- bridge.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions MIT press Cam- bridge

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.259847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation ed5c201b-5f2a-458c-9edf-b350fcb1632d · outbound

This paper cites Mediapipe hands: On-device real- time hand tracking.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Mediapipe hands: On-device real- time hand tracking

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.302108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 4238a97e-a506-4f3e-92f1-b4c062c8a6b0 · outbound

This paper cites Offline-online learning of deformation model for cable manipulation with graph neural networks.IEEE Robotics and Automation Letters, 7(2):5544–5551.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Offline-online learning of deformation model for cable manipulation with graph neural networks.IEEE Robotics and Automation Letters, 7(2):5544–5551

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.385700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 61f48576-4a9c-41fe-a458-cb539154e522 · outbound

This paper cites Equivariant $q$ learning in spatial action spaces.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Equivariant $q$ learning in spatial action spaces

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.369075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 110e2edc-ef02-455c-b69d-cfd2c7b35601 · outbound

This paper cites The Benefits of Model-Based Generalization in Reinforcement Learning.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions The Benefits of Model-Based Generalization in Reinforcement Learning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:40:41.279962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation b3747481-daa9-489f-9fcf-3b0455ae3320 · outbound

This paper cites Tossingbot: Learning to throw arbitrary objects with residual physics.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Tossingbot: Learning to throw arbitrary objects with residual physics

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.378148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 260d98c1-0e37-49b1-bae9-8268b67dbd12 · outbound

This paper cites Adaptigraph: Material-adaptive graph-based neural dynamics for robotic manipulation.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Adaptigraph: Material-adaptive graph-based neural dynamics for robotic manipulation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.346231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation 7552de57-4f5a-4a70-addf-7e0a98fddc43 · outbound

This paper cites Particle-grid neural dynamics for learning deformable object models from rgb-d videos.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Particle-grid neural dynamics for learning deformable object models from rgb-d videos

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.255210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation e1715c58-ab51-4264-af3c-b4b894e96908 · outbound

This paper cites Dy- namic 3d gaussian tracking for graph-based neural dy- namics modeling.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Dy- namic 3d gaussian tracking for graph-based neural dy- namics modeling

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.372133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation cfc59b60-96d2-480f-81a1-8c12cb33f0d2 · outbound

This paper cites Reconstruction and simulation of elastic objects with spring-mass 3D gaussians.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions Reconstruction and simulation of elastic objects with spring-mass 3D gaussians

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.249806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Observation f87c3efd-b992-4080-896f-9f55fde6c852 · outbound

This paper cites We need to prove the following equivalence a=R −(atan2(e−s)+2π)(x−e) =R −(atan2(Rθe+g−(Rθs+g)+2π)(Rθx+g−(R θe+g)).

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions We need to prove the following equivalence a=R −(atan2(e−s)+2π)(x−e) =R −(atan2(Rθe+g−(Rθs+g)+2π)(Rθx+g−(R θe+g))

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T05:36:45.363578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-02T06:30:47.504484+00:00.

Pith citing papers

Observation 0d1d4a3e-fdac-4805-b7c2-21825009995f · inbound

PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics cites this paper.

PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T09:46:45.685374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.