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Paper Citation Record · LEDGER

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition

As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.02574.

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

pith.paper-citation-record.v1
2507.02574 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:30:13.591573Z

measured 25 of 25 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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  • verified fuzzy17
  • unresolved8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9fca49c-47c0-486e-a35a-e3cc8dfee06d · outbound

This paper cites an unresolved cited work.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Unresolved cited work

Reference 1

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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.

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Observation 758cb512-3d16-4519-95ff-df33b76c4fd1 · outbound

This paper cites Here we have explicitly written the likelihood in terms of the Boltzmann-Gibbs distribution Eq.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Here we have explicitly written the likelihood in terms of the Boltzmann-Gibbs distribution Eq

Reference 2

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Observation 590a2f16-13a0-42c4-8eac-3fa3e10d76a9 · outbound

This paper cites (13), and the theoretical average magnetization and correlation, Eqs.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition (13), and the theoretical average magnetization and correlation, Eqs

Reference 3

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation dcc5f84a-1f47-42a2-860d-2feedd461780 · outbound

This paper cites Eventually, {J, h} will converge to the parameter values maximizing the log-Likelihood.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Eventually, {J, h} will converge to the parameter values maximizing the log-Likelihood

Reference 4

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Observation 20e700d2-267d-4926-b970-1a75e304235f · outbound

This paper cites (38), because all sites can be addressed in parallel.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition (38), because all sites can be addressed in parallel

Reference 5

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Observation fd00c194-e7ff-4b8f-b3d0-7e9682aa8694 · outbound

This paper cites an unresolved cited work.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Unresolved cited work

Reference 6

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Observation 10e161aa-e6be-4af3-b8b1-a88732e25246 · outbound

This paper cites an unresolved cited work.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Unresolved cited work

Reference 7

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Observation 574587bd-3fa3-4020-9a19-52fef67a83ec · outbound

This paper cites Li({Ji∂i, hi}) + λJ X j̸=i |Jij| + λhhi # , {J inf ij , hinf i }ℓ2 j∈∂i = argmax {Ji∂i ,hi}.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Li({Ji∂i, hi}) + λJ X j̸=i |Jij| + λhhi # , {J inf ij , hinf i }ℓ2 j∈∂i = argmax {Ji∂i ,hi}

Reference 8

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Observation bfc66d4b-07b3-4db6-9223-d98a9dbf47d5 · outbound

This paper cites (35), as a logistic regression function.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition (35), as a logistic regression function

Reference 9

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 08fd26b0-cbce-4dd0-9c2f-c5dc0a312602 · outbound

This paper cites low dimension.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition low dimension

Reference 10

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Observation 187fc663-96a8-4d7c-8920-30773ba59104 · outbound

This paper cites In formulas, this amount to have vanishing connected correlation functions, Eq.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition In formulas, this amount to have vanishing connected correlation functions, Eq

Reference 11

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Observation 5959145f-aa85-4d75-920d-b42b945cbe72 · outbound

This paper cites Let us start from the Helmholtz free energy, whose equilibrium definition is F ({J, h}) = − 1 β ln Z({J, h}).

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Let us start from the Helmholtz free energy, whose equilibrium definition is F ({J, h}) = − 1 β ln Z({J, h})

Reference 13

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Observation 0c3c931f-54f6-4c19-9f07-7c84892063a5 · outbound

This paper cites an unresolved cited work.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Unresolved cited work

Reference 14

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Observation 5539a7fd-7fa0-4007-95c1-cd184868b9c9 · outbound

This paper cites an unresolved cited work.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Unresolved cited work

Reference 15

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Observation da9aa683-3911-4187-89c5-ecd86dd74fc4 · outbound

This paper cites an unresolved cited work.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Unresolved cited work

Reference 16

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Observation 42235508-7e24-4ac5-9c93-705233a88be6 · outbound

This paper cites 5 in the case of N = 64 spins.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition 5 in the case of N = 64 spins

Reference 17

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Observation 885789f0-118d-437d-9dc2-12edd40ba042 · outbound

This paper cites 6 in the case of N = 64 spins for the vectorial Potts model with q = 4.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition 6 in the case of N = 64 spins for the vectorial Potts model with q = 4

Reference 18

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Observation c428918b-b691-41ce-bd25-e6ebb62bf5c4 · outbound

This paper cites Finding Patient Zero: Learning Contagion Source with Graph Neural Networks.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

Reference 19

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Unavailable: canonical work link unavailable.

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Observation 6c9739a6-40f8-40d2-a90f-fbe4f0a84b5f · outbound

This paper cites (A2) The only difference between the two models is that Ising variables can have value si = ±1, while Blume-Capel spins can also have value zero.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition (A2) The only difference between the two models is that Ising variables can have value si = ±1, while Blume-Capel spins can also have value zero

Reference 20

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Observation d3ee74eb-a5b4-4734-9a0c-ca73456de95c · outbound

This paper cites Indeed, for each color c, we define a magnetization mc as89: mc = fc − Pq r̸=c fr q − 1 = fcq − 1 q − 1 , (A3) where fc is the fraction of spins of color c.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Indeed, for each color c, we define a magnetization mc as89: mc = fc − Pq r̸=c fr q − 1 = fcq − 1 q − 1 , (A3) where fc is the fraction of spins of color c

Reference 21

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Observation c569551b-31f7-4e4f-bb93-b27e22b3f85a · outbound

This paper cites For the Ising model with zero-mean Gaussian couplings on an Erd˝ os–R´ enyi random graph—the Viana–Bray model45—geometric frustration forbids any ferromagnetic order- ing.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition For the Ising model with zero-mean Gaussian couplings on an Erd˝ os–R´ enyi random graph—the Viana–Bray model45—geometric frustration forbids any ferromagnetic order- ing

Reference 22

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Observation 446fc02b-c16e-4d1a-a4a5-15471de0ab9e · outbound

This paper cites an unresolved cited work.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition Unresolved cited work

Reference 23

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Observation 6eaf64f9-e56a-4e6a-8318-900612b7baf5 · outbound

This paper cites In this way the s = 0 is suppressed.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition In this way the s = 0 is suppressed

Reference 24

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Observation 43fadcf1-5318-4c01-b510-41647a03e8bf · outbound

This paper cites (C15) where Z is a normalization required to ensure ν(1) + ν(2) + ν(3) + ν(4) = 1.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition (C15) where Z is a normalization required to ensure ν(1) + ν(2) + ν(3) + ν(4) = 1

Reference 25

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Observation 4b863fc1-3072-4107-9df5-9749febb7c1d · outbound

This paper cites 60 and 61.

Learning and Testing Inverse Statistical Problems For Interacting Systems Undergoing Phase Transition 60 and 61

Reference 77

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