{"as_of":"2026-08-21T01:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:af509d9decab87f2baf7180cc25fe3796304caead6b0ddc01911445be03a84ad","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T12:22:06.831012Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.27061/citation-record","integrity":"/paper/2607.27061/integrity","json":"/paper/2607.27061/citation-record.json","paper":"/paper/2607.27061"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:04.731217Z","title":"The identity X λ⊢n (dimλ) 2 =n! (12) ensures that the measure is normalized","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:04.731217Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:856adfca24b9e9ffb9d4a94221a7b2e34114b031f5ddf3b5ef0b46d134b0cad5","observation_id":"139c3ff7-f3e8-4542-8c3c-42cd5f274b77","resolution":{"observed_at":"2026-07-30T12:22:04.731217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:04.792668Z","title":"No closed-form expression is known, although asymptotic formulas such as the Hardy–Ramanujan formula are available","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:04.792668Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:822d69713603b372e291c4027dbab08b201fdf9b08fd95def6a16035b475e137","observation_id":"63fcd98b-9bc0-4131-9500-1f9794a1f165","resolution":{"observed_at":"2026-07-30T12:22:04.792668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:04.866762Z","title":", 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","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:04.866762Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:be117a23360e1ba5767a8d41998d59b8c9b1f718f6a5c66f74fe37321088a01d","observation_id":"1769732f-c502-4b07-93d3-395f4bfe6cf2","resolution":{"observed_at":"2026-07-30T12:22:04.866762Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.742699Z","title":"Before evaluating the exact finite-size action, these rows are projected onto an integer partition ofn","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.742699Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:3b5c6cffd84ced574d82920d2e15baac90f149956ab44ef72f60e0cfc7b4bb3f","observation_id":"7bce0d64-8e72-49ba-9dd4-9d392f515092","resolution":{"observed_at":"2026-07-30T12:22:05.742699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:04.954130Z","title":"It serves as the main non-benchmark application of the neural variational solver developed in this work","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:04.954130Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:b078081e221d8b9de04fc0bae2555ea4422a6a4964ec239c3e11689c492ce622","observation_id":"52a14b41-e8a9-4ebc-8be9-85b7d1b25184","resolution":{"observed_at":"2026-07-30T12:22:04.954130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.052499Z","title":"The neural density is optimized using the finite- grid Bose-type entropy introduced in Sec","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.052499Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:96d7b4c35f65320920401ac9efc9fece4fd54a9b2b0354b21aa2c5d56f4de8db","observation_id":"e66d5409-2fb0-436b-9d19-73277ff4a77a","resolution":{"observed_at":"2026-07-30T12:22:05.052499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.148942Z","title":"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","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.148942Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:4bb6bad42af4eca4cf25ae5e01998f4cb20e5b28ebf0f2b95a056f99700eb95b","observation_id":"c64d62ae-bb9a-4b1a-9b6b-f5c6b3d2c948","resolution":{"observed_at":"2026-07-30T12:22:05.148942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.235490Z","title":"Quantum Universe Physical Simulation Platform","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.235490Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:9e8c71567c83ddac71424f89b15d3373e120bf4d74675253379f05f6cc4e4156","observation_id":"a1dd5158-89d6-45a5-8838-e36c8cea0b52","resolution":{"observed_at":"2026-07-30T12:22:05.235490Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.356669Z","title":"Linear weights are initialized with Xavier initialization and biases are set to zero","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.356669Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:b83e668c1a4e073ae69969122a497fbcc646d81a2707e4c3f9aa69c08d96432f","observation_id":"4f525e93-6314-4c32-96ff-58821556a336","resolution":{"observed_at":"2026-07-30T12:22:05.356669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.467599Z","title":"Ordinary Plancherel calculation For the ordinary Plancherel benchmark, the retained row coordinates are xi = i√n , i= 1,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.467599Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:a64b8cef6d66d122dcff12044cf3c317b646dc3b964c8f228f273ce3454d7588","observation_id":"6511a299-5caa-4780-8e1b-59b7c5969d47","resolution":{"observed_at":"2026-07-30T12:22:05.467599Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.562240Z","title":"Uniform random partitions For uniform random partitions, we use the grid xk = k√n ,∆x= 1√n , k= 1,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.562240Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:2acac00b37187e1e1201feb324e60d658d1df7810b644fdae565bcceb7334cee","observation_id":"9a49d8a6-48f4-4295-8d9c-533b281dd2ea","resolution":{"observed_at":"2026-07-30T12:22:05.562240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.831125Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.831125Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:59aaa285f3f78c342b7ddaee1244d9dacce65c3a8bdea36b68383b77c303427c","observation_id":"ba9c2a0a-e11c-4cca-9da7-c37f12a00944","resolution":{"observed_at":"2026-07-30T12:22:05.831125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:05.908636Z","title":"A rowicontains a removable corner when λi > λi+1,(A60) where the row below the final nonzero row is assigned length zero","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:05.908636Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:ff768a5de02c03bf2d9e0d5a9adc234bc12faf315e01eacbe7fe4459d8ea1ba8","observation_id":"bbd54f29-7811-4365-ba74-029c10d83956","resolution":{"observed_at":"2026-07-30T12:22:05.908636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.048631Z","title":"Fulton and J","venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.048631Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:84e3dd77417905271b5ef2d382f49bd3da8b34f7ab76212c282644e13c737ec5","observation_id":"46967e9f-bbd3-4307-b703-399edf7a84de","resolution":{"observed_at":"2026-07-30T12:22:06.048631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"quant-ph/0601001","last_updated":"2005-12-30T21:05:56Z","snapshot_observed_at":"2026-07-07T07:21:27.186046Z","submitted_at":"2005-12-30T21:05:56Z","title":"The Quantum Schur Transform: I. Efficient Qudit Circuits","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"quant-ph/0601001","snapshot_observed_at":"2026-07-30T12:22:06.156922Z","title":"Bacon, I","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.156922Z"},"links":{"cited_paper":"/paper/quant-ph/0601001","citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:5944c4254d2429daf1eb1c32070d9d215f15508b897633dcd8453b985b6dc7f9","observation_id":"63009560-b3a2-4976-bab3-1220eeaf1180","resolution":{"observed_at":"2026-07-30T12:22:06.156922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.284800Z","title":null,"venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.284800Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:531789eafda8de999bd2d9980502a9e141dd436c78a52632200490f1577dd715","observation_id":"fad2beab-0f0c-49cd-ab0b-95350b8c47a3","resolution":{"observed_at":"2026-07-30T12:22:06.284800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.425681Z","title":null,"venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.425681Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:f2c20f853e98886f5aae52b67ae13896769dd980e89c21db4876788066bdcb68","observation_id":"65c6742c-9dfd-4c1d-81d3-27f257237965","resolution":{"observed_at":"2026-07-30T12:22:06.425681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.547011Z","title":null,"venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.547011Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:1046a207081f11d2bd6bda568f4795e4a6b363638c2d49d8354cca4362fdadb9","observation_id":"fd4a9abd-5a95-42d8-a5d0-aa4a9b70230f","resolution":{"observed_at":"2026-07-30T12:22:06.547011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.649009Z","title":"Mkrtchyan, European Journal of Combinatorics33, 1631 (2012), groups, Graphs, and Languages","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.649009Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:3a35b9444314ed610afcb407a4a6fdd363f0040a4a65f932ed9b37af24da7c67","observation_id":"86b05de9-ca35-4733-933f-eac13ca32837","resolution":{"observed_at":"2026-07-30T12:22:06.649009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"math/9905032","last_updated":"1999-09-11T23:42:25Z","snapshot_observed_at":"2026-08-15T19:41:32.254239Z","submitted_at":"1999-05-05T17:52:57Z","title":"Asymptotics of Plancherel measures for symmetric groups","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"math/9905032","snapshot_observed_at":"2026-07-30T12:22:06.757305Z","title":"Borodin, A","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.757305Z"},"links":{"cited_paper":"/paper/math/9905032","citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:fb537af8ebcd467335d13a9a653ed6bb5329f963ea10de20b0347218c8b5e9a0","observation_id":"d0eb2242-6d49-460d-8447-75912a5d3f1b","resolution":{"observed_at":"2026-07-30T12:22:06.757305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.791187Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.791187Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:629d1387e7dce63c49c7092752ffabd720592bf689c8043099e0edda428011bb","observation_id":"0b0f4aa8-8686-45af-9ecc-d7f654f17546","resolution":{"observed_at":"2026-07-30T12:22:06.791187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.796969Z","title":null,"venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.796969Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:a57ec66c8707e470c0250b03fa70f9a2f4d2275118d6f79e922d81f6a194089f","observation_id":"b5fc7b75-273e-429d-9268-f477e3d41b1f","resolution":{"observed_at":"2026-07-30T12:22:06.796969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.804044Z","title":"Comtet, S","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.804044Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:44b1d5f9adfa6cfe6c690f61c7da6e9c4f1b1d65fbd94c70bd4cd06305f57def","observation_id":"16d3ec86-2acb-4402-9758-5ac8dd0cae29","resolution":{"observed_at":"2026-07-30T12:22:06.804044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.809670Z","title":"Comtet, S","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.809670Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:fdb45539b3881a7369d026a3a512b25b469eee7a26cf9637d1be5c54dad8bd22","observation_id":"583e2041-7e88-4eba-9b76-89e5f72b734a","resolution":{"observed_at":"2026-07-30T12:22:06.809670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.815424Z","title":"F´ eray and P.-L","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.815424Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:e3611123ff8d92c1b207b846aac978807c58590945c849a9f53ea979056a4854","observation_id":"738fe74c-7ff9-4c4d-a554-b0562bbf75ce","resolution":{"observed_at":"2026-07-30T12:22:06.815424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1009.4047","last_updated":"2010-09-21T10:28:43Z","snapshot_observed_at":"2026-08-15T05:08:14.844152Z","submitted_at":"2010-09-21T10:28:43Z","title":"Asymptotics of the Gelfand models of the symmetric groups","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1009.4047","snapshot_observed_at":"2026-07-30T12:22:06.820016Z","title":"Asymptotics of the gelfand models of the symmetric groups,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.820016Z"},"links":{"cited_paper":"/paper/1009.4047","citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:c86228b04d124abc4a3ea6cf55d7f28af92ccf80cab50f35880d2f4fef8fa56d","observation_id":"ed93f274-06df-4c0d-b7ab-e1bb8fbf8a48","resolution":{"observed_at":"2026-07-30T12:22:06.820016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.825835Z","title":"Metropolis, A","venue":null,"work_id":null,"year":1953},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.825835Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:e44129c9c39385c5343e480abae1b63c9eb5454cdb57aa90e1fb5da23021796e","observation_id":"0639b83c-7596-4558-946d-a38b840801da","resolution":{"observed_at":"2026-07-30T12:22:06.825835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-30T12:22:06.831012Z","title":null,"venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-30T12:22:06.831012Z"},"links":{"citing_paper":"/paper/2607.27061"},"observation_digest":"sha256:71bba77be2821722e7fb382d6a362dfbae631b54235930d9debcb5b38d1d033b","observation_id":"574070d9-24b7-49cb-88e5-f67ad801be01","resolution":{"observed_at":"2026-07-30T12:22:06.831012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.27061","last_updated":"2026-07-29T15:52:00Z","latest_version":1,"primary_category":"cond-mat.stat-mech","snapshot_observed_at":"2026-08-20T23:10:04.039046Z","submitted_at":"2026-07-29T15:52:00Z","title":"Neural variational framework for random Young-diagram limit shapes"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":28},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.27061."}