Paper Citation Record · LEDGER

Neural variational framework for random Young-diagram limit shapes

As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.27061.

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pith.paper-citation-record.v1
2607.27061 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T12:22:06.831012Z

measured 28 of 28 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

28 of 28 outbound references displayed

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Outbound references

Observation 139c3ff7-f3e8-4542-8c3c-42cd5f274b77 · outbound

This paper cites The identity X λ⊢n (dimλ) 2 =n! (12) ensures that the measure is normalized.

Neural variational framework for random Young-diagram limit shapes The identity X λ⊢n (dimλ) 2 =n! (12) ensures that the measure is normalized

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:04.731217Z

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Observation 63fcd98b-9bc0-4131-9500-1f9794a1f165 · outbound

This paper cites No closed-form expression is known, although asymptotic formulas such as the Hardy–Ramanujan formula are available.

Neural variational framework for random Young-diagram limit shapes No closed-form expression is known, although asymptotic formulas such as the Hardy–Ramanujan formula are available

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:04.792668Z

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Observation 1769732f-c502-4b07-93d3-395f4bfe6cf2 · outbound

This paper cites , ℓ(λ)−1}.(17) The corresponding ensemble is uniform on this constrained set: P(p) n (λ) = 1 Zn,p 1{λ∈Y(p) n }, Z n,p =|Y (p) n |,(18) where1 A denotes the indicator of the set A.

Neural variational framework for random Young-diagram limit shapes , ℓ(λ)−1}.(17) The corresponding ensemble is uniform on this constrained set: P(p) n (λ) = 1 Zn,p 1{λ∈Y(p) n }, Z n,p =|Y (p) n |,(18) where1 A denotes the indicator of the set A

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:04.866762Z

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Observation 7bce0d64-8e72-49ba-9dd4-9d392f515092 · outbound

This paper cites Before evaluating the exact finite-size action, these rows are projected onto an integer partition ofn.

Neural variational framework for random Young-diagram limit shapes Before evaluating the exact finite-size action, these rows are projected onto an integer partition ofn

Reference 4

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Observation 52a14b41-e8a9-4ebc-8be9-85b7d1b25184 · outbound

This paper cites It serves as the main non-benchmark application of the neural variational solver developed in this work.

Neural variational framework for random Young-diagram limit shapes It serves as the main non-benchmark application of the neural variational solver developed in this work

Reference 5

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Observation e66d5409-2fb0-436b-9d19-73277ff4a77a · outbound

This paper cites The neural density is optimized using the finite- grid Bose-type entropy introduced in Sec.

Neural variational framework for random Young-diagram limit shapes The neural density is optimized using the finite- grid Bose-type entropy introduced in Sec

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:05.052499Z

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Observation c64d62ae-bb9a-4b1a-9b6b-f5c6b3d2c948 · outbound

This paper cites The constraint λi −λ i+1 ≥p introduces an increasing degree of exclusion between neighboring row lengths;p= 1 corresponds to partitions into distinct parts.

Neural variational framework for random Young-diagram limit shapes The constraint λi −λ i+1 ≥p introduces an increasing degree of exclusion between neighboring row lengths;p= 1 corresponds to partitions into distinct parts

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:05.148942Z

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Observation a1dd5158-89d6-45a5-8838-e36c8cea0b52 · outbound

This paper cites Quantum Universe Physical Simulation Platform.

Neural variational framework for random Young-diagram limit shapes Quantum Universe Physical Simulation Platform

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:05.235490Z

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Observation 4f525e93-6314-4c32-96ff-58821556a336 · outbound

This paper cites Linear weights are initialized with Xavier initialization and biases are set to zero.

Neural variational framework for random Young-diagram limit shapes Linear weights are initialized with Xavier initialization and biases are set to zero

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:05.356669Z

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Observation 6511a299-5caa-4780-8e1b-59b7c5969d47 · outbound

This paper cites Ordinary Plancherel calculation For the ordinary Plancherel benchmark, the retained row coordinates are xi = i√n , i= 1,.

Neural variational framework for random Young-diagram limit shapes Ordinary Plancherel calculation For the ordinary Plancherel benchmark, the retained row coordinates are xi = i√n , i= 1,

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-07-30T12:22:05.467599Z

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Observation 9a49d8a6-48f4-4295-8d9c-533b281dd2ea · outbound

This paper cites Uniform random partitions For uniform random partitions, we use the grid xk = k√n ,∆x= 1√n , k= 1,.

Neural variational framework for random Young-diagram limit shapes Uniform random partitions For uniform random partitions, we use the grid xk = k√n ,∆x= 1√n , k= 1,

Reference 11

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Observation ba9c2a0a-e11c-4cca-9da7-c37f12a00944 · outbound

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Neural variational framework for random Young-diagram limit shapes Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:05.831125Z

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Observation bbd54f29-7811-4365-ba74-029c10d83956 · outbound

This paper cites A rowicontains a removable corner when λi > λi+1,(A60) where the row below the final nonzero row is assigned length zero.

Neural variational framework for random Young-diagram limit shapes A rowicontains a removable corner when λi > λi+1,(A60) where the row below the final nonzero row is assigned length zero

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:05.908636Z

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Observation 46967e9f-bbd3-4307-b703-399edf7a84de · outbound

This paper cites Fulton and J.

Neural variational framework for random Young-diagram limit shapes Fulton and J

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.048631Z

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Observation 63009560-b3a2-4976-bab3-1220eeaf1180 · outbound

This paper cites The Quantum Schur Transform: I. Efficient Qudit Circuits.

Neural variational framework for random Young-diagram limit shapes The Quantum Schur Transform: I. Efficient Qudit Circuits

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.156922Z

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Observation fad2beab-0f0c-49cd-ab0b-95350b8c47a3 · outbound

This paper cites an unresolved cited work.

Neural variational framework for random Young-diagram limit shapes Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.284800Z

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Observation 65c6742c-9dfd-4c1d-81d3-27f257237965 · outbound

This paper cites an unresolved cited work.

Neural variational framework for random Young-diagram limit shapes Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.425681Z

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Observation fd4a9abd-5a95-42d8-a5d0-aa4a9b70230f · outbound

This paper cites an unresolved cited work.

Neural variational framework for random Young-diagram limit shapes Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.547011Z

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Observation 86b05de9-ca35-4733-933f-eac13ca32837 · outbound

This paper cites Mkrtchyan, European Journal of Combinatorics33, 1631 (2012), groups, Graphs, and Languages.

Neural variational framework for random Young-diagram limit shapes Mkrtchyan, European Journal of Combinatorics33, 1631 (2012), groups, Graphs, and Languages

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.649009Z

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Observation d0eb2242-6d49-460d-8447-75912a5d3f1b · outbound

This paper cites Asymptotics of Plancherel measures for symmetric groups.

Neural variational framework for random Young-diagram limit shapes Asymptotics of Plancherel measures for symmetric groups

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.757305Z

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Observation 0b0f4aa8-8686-45af-9ecc-d7f654f17546 · outbound

This paper cites an unresolved cited work.

Neural variational framework for random Young-diagram limit shapes Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.791187Z

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Observation b5fc7b75-273e-429d-9268-f477e3d41b1f · outbound

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Neural variational framework for random Young-diagram limit shapes Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.796969Z

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Observation 16d3ec86-2acb-4402-9758-5ac8dd0cae29 · outbound

This paper cites Comtet, S.

Neural variational framework for random Young-diagram limit shapes Comtet, S

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.804044Z

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Observation 583e2041-7e88-4eba-9b76-89e5f72b734a · outbound

This paper cites Comtet, S.

Neural variational framework for random Young-diagram limit shapes Comtet, S

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.809670Z

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Observation 738fe74c-7ff9-4c4d-a554-b0562bbf75ce · outbound

This paper cites F´ eray and P.-L.

Neural variational framework for random Young-diagram limit shapes F´ eray and P.-L

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.815424Z

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Observation ed93f274-06df-4c0d-b7ab-e1bb8fbf8a48 · outbound

This paper cites Asymptotics of the Gelfand models of the symmetric groups.

Neural variational framework for random Young-diagram limit shapes Asymptotics of the Gelfand models of the symmetric groups

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.820016Z

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Observation 0639b83c-7596-4558-946d-a38b840801da · outbound

This paper cites Metropolis, A.

Neural variational framework for random Young-diagram limit shapes Metropolis, A

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.825835Z

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Observation 574070d9-24b7-49cb-88e5-f67ad801be01 · outbound

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Neural variational framework for random Young-diagram limit shapes Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-30T12:22:06.831012Z

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