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Paper Citation Record · LEDGER

Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.13300.

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

pith.paper-citation-record.v1
2210.13300 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:33:45.291803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T03:33:50.947441Z

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 6e0422e1-5d96-49e3-8926-ded02f9acf20 · inbound

Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces cites this paper.

Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-24T03:33:50.950565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-24T03:30:28.927126Z digest=sha256:070d3496f02341958a667abbb7f209a253b6f7d40726f636783fa251b7b4c5c1

Observation 31406db7-1b22-4fe4-a367-f44d14ca23f8 · inbound

Neural Operators Can Play Dynamic Stackelberg Games cites this paper.

Neural Operators Can Play Dynamic Stackelberg Games Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-12T20:33:45.291803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T20:33:45.291803Z digest=sha256:76c8e72e0458cb32be310799a89bc088602e4df154e460c4c798c5c575883e34