Paper Citation Record · LEDGER

Mixtures of Neural Operators Reduce Active Complexity in Operator Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.09101.

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

pith.paper-citation-record.v1
2404.09101 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:33:45.261913Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T05:07:34.893977Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
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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 6851e8d1-1e2a-46e8-beb0-f33792c9c171 · inbound

Neural Operators Can Play Dynamic Stackelberg Games cites this paper.

Neural Operators Can Play Dynamic Stackelberg Games Mixtures of Neural Operators Reduce Active Complexity in Operator Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T20:33:45.261913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79903a88-a31f-45ea-9dda-00ab84e19ced · inbound

Neural equilibria for long-term prediction of nonlinear conservation laws cites this paper.

Neural equilibria for long-term prediction of nonlinear conservation laws Mixtures of Neural Operators Reduce Active Complexity in Operator Learning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-06-10T02:10:29.704823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63d02917-9a37-4b56-b1ee-d71610807a9e · inbound

Upper Approximation Bounds for Neural Oscillators cites this paper.

Upper Approximation Bounds for Neural Oscillators Mixtures of Neural Operators Reduce Active Complexity in Operator Learning

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-06-10T02:10:29.704823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e39e013c-39cb-4397-bf77-82b726c1c481 · inbound

Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System cites this paper.

Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System Mixtures of Neural Operators Reduce Active Complexity in Operator Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-10T02:10:29.704823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.