Paper Citation Record · LEDGER

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting

As of 17 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2606.07670.

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

pith.paper-citation-record.v1
2606.07670 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T02:10:50.808449Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

17 of 17 outbound references displayed

verified exact2
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b8be2177-cabd-4f35-921e-a43558e7b436 · outbound

This paper cites D-NeRF: Neural radiance fields for dynamic scenes,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting D-NeRF: Neural radiance fields for dynamic scenes,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7401870-64d5-40a3-89ca-7bf533708605 · outbound

This paper cites 3D Gaussian Splatting for real-time radiance field ren- dering,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting 3D Gaussian Splatting for real-time radiance field ren- dering,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee9d97c2-a878-4237-aaba-82ebc5d9c463 · outbound

This paper cites Deformable 3D Gaussians for high-fidelity monocular dynamic scene reconstruction,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Deformable 3D Gaussians for high-fidelity monocular dynamic scene reconstruction,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed050f7d-2efe-45d8-9085-8dd83d2d9b76 · outbound

This paper cites Neural ordinary differential equations,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Neural ordinary differential equations,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47f33e07-cab1-4b36-8d4a-a3e984e6b9d8 · outbound

This paper cites ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-02T12:26:56.305389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 10849352-b520-4d29-ac5f-92b7c53dce4f · outbound

This paper cites Closed-form continuous-time neural networks,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Closed-form continuous-time neural networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb758ae9-a742-4521-a169-1fab0278bd81 · outbound

This paper cites Liquid Time-constant Networks.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Liquid Time-constant Networks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:26:56.302479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a7ac8a8-a656-4908-965c-437b1b1d72e1 · outbound

This paper cites Liquid neural networks: A novel approach to dynamic infor- mation processing,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Liquid neural networks: A novel approach to dynamic infor- mation processing,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 290645ec-2995-4a97-838e-d973ee0f62de · outbound

This paper cites A general- ized framework for liquid neural networks upon sequen- tial and non-sequential tasks,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting A general- ized framework for liquid neural networks upon sequen- tial and non-sequential tasks,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbaccc0a-4e86-489f-a6b3-15b23144185c · outbound

This paper cites Sovrasov,Ptflops: A flops counting tool for neural networks in PyTorch, https://github.com/sovrasov/flops- counter.pytorch, 2018.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Sovrasov,Ptflops: A flops counting tool for neural networks in PyTorch, https://github.com/sovrasov/flops- counter.pytorch, 2018

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07c3f05b-c97e-4250-a040-bafaf814d49d · outbound

This paper cites Gaussian-Flow: 4d reconstruction with dynamic 3d gaussian particle,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Gaussian-Flow: 4d reconstruction with dynamic 3d gaussian particle,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc02375a-4a11-46d3-a7d7-5929c5c9251a · outbound

This paper cites Shape of motion: 4d reconstruction from a single video,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Shape of motion: 4d reconstruction from a single video,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5c677ff-3d64-438d-8230-5f0cd380d3c5 · outbound

This paper cites FLAG-4D: Flow-guided local-global dual-deformation model for 4D reconstruction,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting FLAG-4D: Flow-guided local-global dual-deformation model for 4D reconstruction,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 481ba958-8a2b-486a-ba67-d48794eeecb7 · outbound

This paper cites Scalable gradients for stochastic differential equations,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Scalable gradients for stochastic differential equations,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9e60aed-1e69-4a3b-93be-281571b93361 · outbound

This paper cites Liquid structural state-space mod- els,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Liquid structural state-space mod- els,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f65490a-39f2-44e9-b60f-8e09f7321c0a · outbound

This paper cites NeRF-DS: Neural radiance fields for dynamic specular objects,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting NeRF-DS: Neural radiance fields for dynamic specular objects,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d305da7c-9209-4078-afc3-bee7a40c81d2 · outbound

This paper cites Fast dynamic radiance fields with time- aware neural voxels,.

Liquid Neural Networks as a Drop-in Continuous-Time Deformation Field for Dynamic 3D Gaussian Splatting Fast dynamic radiance fields with time- aware neural voxels,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-28T02:10:50.808449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.