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Paper Citation Record · LEDGER

Towards Identifiability of Interventional Stochastic Differential Equations

As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 4 inbound Pith citation observations for arXiv:2505.15987.

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

pith.paper-citation-record.v1
2505.15987 v5

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:17:26.560611Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T04:27:28.819903Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T16:55:51.001001Z

Reference resolution

16 of 16 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 08648bd8-258e-4aa2-95ef-d4c507bf990d · outbound

This paper cites Identifying drift, diffusion, and causal structure from temporal snap- shots.arXiv preprint arXiv:2410.22729,.

Towards Identifiability of Interventional Stochastic Differential Equations Identifying drift, diffusion, and causal structure from temporal snap- shots.arXiv preprint arXiv:2410.22729,

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 268756b1-10b2-4d07-829c-2ae3a4045f50 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Towards Identifiability of Interventional Stochastic Differential Equations Adam: A Method for Stochastic Optimization

Reference 5

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Unavailable: canonical work link unavailable.

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Observation 5d223e50-264c-4a3a-9654-ab96b85b3588 · outbound

This paper cites Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations.

Towards Identifiability of Interventional Stochastic Differential Equations Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations

Reference 7

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8a6d5754-0cac-46b8-bbe2-4a0fc2a453c5 · outbound

This paper cites Decoupled Weight Decay Regularization.

Towards Identifiability of Interventional Stochastic Differential Equations Decoupled Weight Decay Regularization

Reference 9

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unresolved
no resolver link, observed 2026-08-07T15:17:26.143625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b61aa33e-e7fb-4f1f-b449-e1bce82bfcc1 · outbound

This paper cites an unresolved cited work.

Towards Identifiability of Interventional Stochastic Differential Equations Unresolved cited work

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 1e2ee67c-c866-4338-ba35-d248b9ad66a5 · outbound

This paper cites With this setup, we obtain a family of distributions{ρ k}K k=0 in gene expression space.

Towards Identifiability of Interventional Stochastic Differential Equations With this setup, we obtain a family of distributions{ρ k}K k=0 in gene expression space

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 1845e202-eac6-4b5b-a3a4-ba29b2be0579 · outbound

This paper cites Each interventional distributionρ k is obtained by taking the observational dataρ 0 as initial distribu- tion and simulating the SDE via the Euler-Maruyama method.

Towards Identifiability of Interventional Stochastic Differential Equations Each interventional distributionρ k is obtained by taking the observational dataρ 0 as initial distribu- tion and simulating the SDE via the Euler-Maruyama method

Reference 16

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c7ae7b59-b754-4630-8276-c6fc8d636c71 · outbound

This paper cites an unresolved cited work.

Towards Identifiability of Interventional Stochastic Differential Equations Unresolved cited work

Reference 20

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unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 243288d9-eb06-496b-9bb2-ccdc4c297fd2 · outbound

This paper cites Gaussian Approximations of Small Noise Diffusions in Kullback-Leibler Divergence.

Towards Identifiability of Interventional Stochastic Differential Equations Gaussian Approximations of Small Noise Diffusions in Kullback-Leibler Divergence

Reference 2005

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unresolved
no resolver link, observed 2026-08-07T15:17:26.194966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8e6b44ac-6193-4812-8763-9b21fa90d7c4 · outbound

This paper cites Regvelo: gene-regulatory-informed dynamics of single cells.bioRxiv, pp.

Towards Identifiability of Interventional Stochastic Differential Equations Regvelo: gene-regulatory-informed dynamics of single cells.bioRxiv, pp

Reference 2014

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 050b8d0e-95ad-41cf-b435-002ca5083494 · outbound

This paper cites Efficient matrix sensing using rank-1 gaussian measurements.

Towards Identifiability of Interventional Stochastic Differential Equations Efficient matrix sensing using rank-1 gaussian measurements

Reference 2018

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 27e9bd8c-b5c0-4624-b9e7-7f8b81ec3f2b · outbound

This paper cites Long-time dynamics of stochastic differential equations.

Towards Identifiability of Interventional Stochastic Differential Equations Long-time dynamics of stochastic differential equations

Reference 2019

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unresolved
no resolver link, observed 2026-08-07T15:17:25.573409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 99352312-3096-40f6-8df6-bd8ba47401fc · outbound

This paper cites Learning Activation Functions: A new paradigm for understanding Neural Networks.

Towards Identifiability of Interventional Stochastic Differential Equations Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 2021

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unresolved
no resolver link, observed 2026-08-07T15:17:25.775083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c3b82730-31ce-4fd7-a5a7-f258f8390e3d · outbound

This paper cites Scaling structural learning with no-bears to infer causal transcriptome networks.

Towards Identifiability of Interventional Stochastic Differential Equations Scaling structural learning with no-bears to infer causal transcriptome networks

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:17:28.160652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9617002e-a268-4b38-86dd-293dd78bd001 · outbound

This paper cites The power of convex relaxation: Near-optimal matrix com- pletion.IEEE transactions on information theory, 56(5):2053–2080,.

Towards Identifiability of Interventional Stochastic Differential Equations The power of convex relaxation: Near-optimal matrix com- pletion.IEEE transactions on information theory, 56(5):2053–2080,

Reference 2024

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f7a7b351-fd79-4097-8b04-1a4917690b95 · outbound

This paper cites Causal modeling with stationary diffusions.

Towards Identifiability of Interventional Stochastic Differential Equations Causal modeling with stationary diffusions

Reference 2025

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Pith citing papers

Observation 61d78244-61f6-4e30-8a0a-e6de4fdd14a5 · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots Towards Identifiability of Interventional Stochastic Differential Equations

Reference 48

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verified exact
arxiv_id, observed 2026-07-13T02:18:50.803032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 50b7b346-f9f2-43ca-9864-0fa486abdedf · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots Towards Identifiability of Interventional Stochastic Differential Equations

Reference 48

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verified exact
arxiv_id, observed 2026-07-13T02:18:50.803032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 7791955d-79d2-4d42-b15b-bae48ad18bee · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots Towards Identifiability of Interventional Stochastic Differential Equations

Reference 48

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verified exact
arxiv_id, observed 2026-07-13T02:18:50.803032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-20T22:20:03.150386Z digest=sha256:46b5db39db1221461eb8ed4697b511d59912edea96b793c4e1680d476c0d4650

Observation a2d3a699-3957-4120-8671-a937d9f83fe1 · inbound

Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts cites this paper.

Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts Towards Identifiability of Interventional Stochastic Differential Equations

Reference 50

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arxiv_id, observed 2026-07-13T02:18:50.803032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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