Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T22:13:22.800077Z
Paper Citation Record · LEDGER
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
As of 10 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 6 inbound Pith citation observations for arXiv:2501.18871.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:31:41.621173Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T19:01:46.342488Z
Reference resolution
21 of 21 outbound references displayed
- verified exact1
- verified fuzzy5
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 0b28912a-40dc-4665-ab8b-830f1763b67e · outbound
This paper cites Numerical solutions of stochastic differen- tial equations (kloeden, pk and platen, e.; 2008)[book reviews].
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Numerical solutions of stochastic differen- tial equations (kloeden, pk and platen, e.; 2008)[book reviews]
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T22:13:23.214142Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 51e26572-e842-4da7-b6a0-96ebcc3a63b8 · outbound
This paper cites and Ziou, D.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling and Ziou, D
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T22:13:23.196720Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation da800ae8-50f9-49b1-a488-235ec5912461 · outbound
This paper cites Optimal Flow Matching: Learning Straight Trajectories in Just One Step.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Optimal Flow Matching: Learning Straight Trajectories in Just One Step
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.746837Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fcaf88f4-97e9-417e-86d6-83ba7afc2354 · outbound
This paper cites Flow Matching for Generative Modeling.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Flow Matching for Generative Modeling
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.753692Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8ef4cf3f-ba51-4fda-995a-a37b9d8d9b42 · outbound
This paper cites I$^2$SB: Image-to-Image Schr\"odinger Bridge.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling I$^2$SB: Image-to-Image Schr\"odinger Bridge
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.759126Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 671da4de-1538-4875-a665-aea25508267e · outbound
This paper cites Learning Continuous-Time Dynamics by Stochastic Differential Networks.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Learning Continuous-Time Dynamics by Stochastic Differential Networks
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-08-09T22:13:22.917043Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 42df1e2c-d8b3-43dd-9662-20a4994bf744 · outbound
This paper cites Sequence to Sequence Learning with Neural Networks.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Sequence to Sequence Learning with Neural Networks
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.777127Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3d87d773-424e-4fc3-a771-8f84e661317e · outbound
This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Improving and generalizing flow-based generative models with minibatch optimal transport
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.788690Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9c602529-74b5-4d4d-8420-bfb83a070489 · outbound
This paper cites log c2 i fi(xt) − ∆xi ∆ti 2!# = 1 2 dX i=1.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling log c2 i fi(xt) − ∆xi ∆ti 2!# = 1 2 dX i=1
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T22:13:23.159633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 36b7ef65-4e7d-48d8-857f-d5d21951b5b6 · outbound
This paper cites shortcut.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling shortcut
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T22:13:23.139211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3dc32558-914f-4f08-b672-f2334331fadb · outbound
This paper cites an unresolved cited work.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Unresolved cited work
Reference 2004
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T22:13:23.177601Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b41064ae-5f9e-451b-8c08-86ed396ea4c0 · outbound
This paper cites Generating Sequences With Recurrent Neural Networks.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Generating Sequences With Recurrent Neural Networks
Reference 2008
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.718331Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bfb1b095-2649-4110-9f35-723f8f4aada5 · outbound
This paper cites Nu- merical methods for simulation of stochastic differential equations.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Nu- merical methods for simulation of stochastic differential equations
Reference 2009
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-09T22:13:23.255553Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6ea67dba-0f9c-42fc-841d-924988666e5d · outbound
This paper cites AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies
Reference 2010
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.729099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05b2ebf4-0839-462c-aeb9-a1ad5edb4a7b · outbound
This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
Reference 2014
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.783271Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c7f744c-561a-4a6b-ad1b-ca0ef3d50347 · outbound
This paper cites Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.701265Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9b6f723d-99a1-4ecc-b04c-38c581299d51 · outbound
This paper cites SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.740263Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ddeaaef9-21de-4fb3-a1d3-a16b45efbff4 · outbound
This paper cites Density estimation using Real NVP.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Density estimation using Real NVP
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.707339Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6506ee04-3480-43c1-a764-543af7a6e913 · outbound
This paper cites Stochastic interpolants with data-dependent couplings.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Stochastic interpolants with data-dependent couplings
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.681250Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3191f5f-15ed-4550-9407-0b92b3e67204 · outbound
This paper cites an unresolved cited work.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Unresolved cited work
Reference 2023
- Resolution
- unresolved
- raw_fallback, observed 2026-08-09T22:13:23.234417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c544a096-6dff-43ba-91e7-1aa0a522d14f · outbound
This paper cites Scaling Laws for Neural Language Models.
Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling Scaling Laws for Neural Language Models
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T22:13:22.734509Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 8d25f1fd-7f97-491e-ba42-4725c21038f4 · inbound
Beyond Equilibrium: Non-Equilibrium Foundations Should Underpin Generative Processes in Complex Dynamical Systems cites this paper.
Beyond Equilibrium: Non-Equilibrium Foundations Should Underpin Generative Processes in Complex Dynamical Systems Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
Reference 148
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:31:41.621173Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5080cddf-106b-4fb3-8240-5f28e5f3108f · inbound
Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.
Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
Reference 81
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T19:01:46.345059Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c64ebbb1-e3b2-41ae-8d0a-27f378eda177 · inbound
Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation cites this paper.
Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
Reference 81
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:25:17.220600Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91cbf0c7-8e02-48a3-9640-0872cd2e5f2c · inbound
Deep Neural Networks Inspired by Differential Equations cites this paper.
Deep Neural Networks Inspired by Differential Equations Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
Reference 220
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T10:54:39.127525Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c9b6e1c-93b5-4510-9ffb-598b567b7bad · inbound
The Transformer as a Polar State Estimator cites this paper.
The Transformer as a Polar State Estimator Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
Reference 178
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T02:17:07.535663Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 021292bb-9700-427e-aac8-d0f8525cae58 · inbound
Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise cites this paper.
Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T13:17:13.986691Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.