Pith. sign in

Paper Citation Record · LEDGER

Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.15482.

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

pith.paper-citation-record.v1
2401.15482 v2

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T03:54:18.591111Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T19:38:10.634710Z

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 eb578b8b-fe6c-4e34-9650-13cd33e57577 · inbound

Neural Operators for Multi-Task Control and Adaptation cites this paper.

Neural Operators for Multi-Task Control and Adaptation Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-13T19:38:10.636288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T19:34:04.212168Z digest=sha256:b64767661c031ccc91ea6e0420f377a217061e5396a6364f0bad56235e2908ef

Observation aa076ae3-3cbd-46b7-96d4-70b7e367a0d6 · inbound

All in One: Generative Modeling as Mean-Field Game Design cites this paper.

All in One: Generative Modeling as Mean-Field Game Design Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-01T03:54:18.591111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T03:54:18.591111Z digest=sha256:ee6eec413a27c6f85b3e927229d8001fb9c66bdc84998243fe4f3e02db6cff13