Pith. sign in

Paper Citation Record · LEDGER

Monte Carlo simulations on the Lefschetz thimble: taming the sign problem

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1303.7204.

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

pith.paper-citation-record.v1
1303.7204 v2

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 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:33:39.654802Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-19T03:55:54.561284Z

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 421f97e0-a735-4934-b282-b53b4b13ba22 · inbound

Exploring Group Convolutional Networks for Sign Problem Mitigation via Contour Deformation cites this paper.

Exploring Group Convolutional Networks for Sign Problem Mitigation via Contour Deformation Monte Carlo simulations on the Lefschetz thimble: taming the sign problem

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-08T23:33:39.654802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T23:33:39.654802Z digest=sha256:3761805d7ad34fe34dea9d9693904afa728e51ad932ee423bb525cbb912c25b6

Observation b4474fcf-c318-4870-97b4-246a7470699f · inbound

Applying the Worldvolume Hybrid Monte Carlo method to the Hubbard model away from half filling cites this paper.

Applying the Worldvolume Hybrid Monte Carlo method to the Hubbard model away from half filling Monte Carlo simulations on the Lefschetz thimble: taming the sign problem

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-05-19T02:11:59.249754Z

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.

source=pdf_text observed=2026-05-19T02:10:44.624977Z digest=sha256:c22150438ea29801acdd626819b732a8a2c87d75afe2f58957118b7d57c7cf26

Observation 4b936eff-b77a-4fa9-8cc1-59e94f9edded · inbound

Enhancing the ergodicity of Worldvolume HMC via embedding generalized thimble HMC cites this paper.

Enhancing the ergodicity of Worldvolume HMC via embedding generalized thimble HMC Monte Carlo simulations on the Lefschetz thimble: taming the sign problem

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-05-19T00:41:56.141520Z

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.

source=pdf_text observed=2026-05-19T00:38:22.644910Z digest=sha256:f2a20e6b5d70cb6af389c4a12598b68ef3cb857cda5d3b2dc5567d4344c79b0b

Observation e95b3f06-3edb-4817-a1c1-ebb7a0c07d90 · inbound

Analyzing the two-dimensional doped Hubbard model with the Worldvolume HMC method cites this paper.

Analyzing the two-dimensional doped Hubbard model with the Worldvolume HMC method Monte Carlo simulations on the Lefschetz thimble: taming the sign problem

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-05-15T03:09:43.669672Z

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.

source=pdf_text observed=2026-05-15T03:05:18.671753Z digest=sha256:3c134ce7061f45e3e7cb41806983bc859ce5388bb9b2fbc5e8e8fba4d8338c4c