Paper Citation Record · LEDGER

Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2403.13724.

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

pith.paper-citation-record.v1
2403.13724 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:13:22.701265Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:47:18.912897Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1c7f744c-561a-4a6b-ad1b-ca0ef3d50347 · inbound

Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling cites this paper.

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 ea185b82-ec72-40c5-bd2a-a3de33bebf8a · inbound

Non-stationary Diffusion For Probabilistic Time Series Forecasting cites this paper.

Non-stationary Diffusion For Probabilistic Time Series Forecasting Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T16:51:47.998590Z

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 c1c9af9d-2d9b-439b-a5e6-ef6a8c0ace69 · inbound

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach cites this paper.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 202

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:50.364481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0a5e21-f71a-4904-bca0-029a3f8c238c · inbound

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants cites this paper.

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T23:03:39.142649Z

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 f9cb7d0c-eb85-4f82-b652-971e015b64e3 · inbound

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations cites this paper.

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T09:40:48.848620Z

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 9edd760d-6659-44ba-abb8-46d110f70d9b · inbound

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations cites this paper.

Pathwise Learning of Stochastic Dynamical Systems with Partial Observations Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T06:52:54.695191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 802c1f4b-6744-49e7-a551-213ee86b951c · inbound

Is Flow Matching Just Trajectory Replay for Sequential Data? cites this paper.

Is Flow Matching Just Trajectory Replay for Sequential Data? Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T06:22:27.428273Z

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 7749e3bb-2235-4cf5-911f-25a7c2b5bb35 · inbound

Generative Modeling via Kernelized Stochastic Interpolants cites this paper.

Generative Modeling via Kernelized Stochastic Interpolants Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T21:32:19.272053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed57b383-19e2-4ab4-81b9-7ec0653076ad · inbound

Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models cites this paper.

Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:41:19.183556Z

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 04f64672-5ee9-4874-a69f-726c626faac1 · inbound

ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space cites this paper.

ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:46:26.283777Z

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 3f8da7c6-5959-405a-8d90-6bfb00d9263f · inbound

Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference cites this paper.

Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:25.881548Z

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 f58f3400-e112-4d8e-a67a-be78f5236636 · inbound

TRIE: An Evaluation Framework for Stochastic PDE Surrogates cites this paper.

TRIE: An Evaluation Framework for Stochastic PDE Surrogates Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T19:47:18.914806Z

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.