Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2201.02564.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:57:49.845087Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T10:20:50.023954Z
Reference resolution
0 of 0 outbound references displayed
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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 92c9c3d4-b1eb-452a-bda7-acb81984b08b · inbound
Deep learning for exploring hadron-hadron interactions cites this paper.
Deep learning for exploring hadron-hadron interactions Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:57:49.845087Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d618992a-b70e-4d64-9b0f-ba3757ea7e26 · inbound
Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis cites this paper.
Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:01:08.784703Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a374dcb7-b59f-4bbe-ba25-05d5b274bd07 · inbound
Nucleon axial-vector form factor and radius from radiatively-corrected antineutrino scattering data cites this paper.
Nucleon axial-vector form factor and radius from radiatively-corrected antineutrino scattering data Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help
Reference 94
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T10:20:50.025605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.