Pith. sign in

Paper Citation Record · LEDGER

Towards a Foundation Model for Neural Network Wavefunctions

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2303.09949.

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

pith.paper-citation-record.v1
2303.09949 v1

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 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:34:51.505343Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e965e3ce-5e83-4e66-a5a7-3c0d0ca250bb · inbound

Approximating Hartree-Fock theory via an efficiently local reformulation cites this paper.

Approximating Hartree-Fock theory via an efficiently local reformulation Towards a Foundation Model for Neural Network Wavefunctions

Reference 291

Resolution
verified exact
arxiv_id, observed 2026-06-28T12:02:06.418556Z

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=arxiv_source observed=2026-06-28T11:57:54.741334Z digest=sha256:e7a7a533af16804be297c80a92168c35846547e84780cddcfd950b97097041bd

Observation cca883d3-07c2-480e-adf0-8ee1d2a9a039 · inbound

Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization cites this paper.

Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization Towards a Foundation Model for Neural Network Wavefunctions

Reference 35

Resolution
metadata mismatch
arxiv_id, observed 2026-06-27T04:30:32.814261Z

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=2026-06-25T19:39:51.888228Z digest=sha256:d4071ef94647ff6b6d39129a28a4787c5641eaeb04d7f52f7964685c6aebce2d

Observation ad4540c0-5e0c-4d43-8823-ec47506becae · inbound

Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization cites this paper.

Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization Towards a Foundation Model for Neural Network Wavefunctions

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-02T10:19:10.142801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T10:19:10.142801Z digest=sha256:927322616d7331c7480c0cc270dcad5fa2515320e45a20e0a94f59d2fdb04d39

Observation df7c58f9-0853-4f90-925a-52a030c6bf2f · inbound

Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians cites this paper.

Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians Towards a Foundation Model for Neural Network Wavefunctions

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-16T00:34:51.505343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:34:51.505343Z digest=sha256:3639a8c9854a766e4cd80bf0ad8a5b5c2f4b153580abe076e2670072e4abdde0