{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SFMARC4AWLQA6QKQLPJUYDSFS5","short_pith_number":"pith:SFMARC4A","schema_version":"1.0","canonical_sha256":"9158088b80b2e00f41505bd34c0e459743b1ec79417c18a670400bc2d3ba93ff","source":{"kind":"arxiv","id":"2502.02711","version":5},"attestation_state":"computed","paper":{"title":"Tensor Network Structure Search Via Canonical Dimension Tree Enumeration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PL"],"primary_cat":"cs.CE","authors_text":"Aditya Deshpande, Alex Gorodetsky, Brian Kiedrowski, Xinyu Wang, Zheng Guo","submitted_at":"2025-02-04T20:46:37Z","abstract_excerpt":"Tensor networks provide a powerful framework for compressing multi-dimensional data. The optimal tensor network structure for a given data tensor depends on both data characteristics and specific optimality criteria, making tensor network structure search a challenging problem. Existing solutions typically rely on sampling and compressing numerous candidate structures; these procedures are computationally expensive and therefore limiting for practical applications. We address this challenge by decoupling topology enumeration from rank assignment search. We first represent the search space usin"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.02711","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2025-02-04T20:46:37Z","cross_cats_sorted":["cs.PL"],"title_canon_sha256":"cbe8e86ea31721136da3689a188574afd1e77bbff5cb5745caf588e84d3a962a","abstract_canon_sha256":"b7809e91305701123dadc542bb611ca8254799952b2145197543aeb936f8c25b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:19:04.736621Z","signature_b64":"H4JX8+oRZtvWS61nSMyo2CGDqjtUplQV1EVRuwFeUlcBI86Lt+0Rabkq4oelSmZvZ7PVe+a9/NaLIkWN30RJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9158088b80b2e00f41505bd34c0e459743b1ec79417c18a670400bc2d3ba93ff","last_reissued_at":"2026-07-08T01:19:04.736077Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:19:04.736077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tensor Network Structure Search Via Canonical Dimension Tree Enumeration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.PL"],"primary_cat":"cs.CE","authors_text":"Aditya Deshpande, Alex Gorodetsky, Brian Kiedrowski, Xinyu Wang, Zheng Guo","submitted_at":"2025-02-04T20:46:37Z","abstract_excerpt":"Tensor networks provide a powerful framework for compressing multi-dimensional data. The optimal tensor network structure for a given data tensor depends on both data characteristics and specific optimality criteria, making tensor network structure search a challenging problem. Existing solutions typically rely on sampling and compressing numerous candidate structures; these procedures are computationally expensive and therefore limiting for practical applications. We address this challenge by decoupling topology enumeration from rank assignment search. We first represent the search space usin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02711","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.02711/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.02711","created_at":"2026-07-08T01:19:04.736131+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.02711v5","created_at":"2026-07-08T01:19:04.736131+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02711","created_at":"2026-07-08T01:19:04.736131+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFMARC4AWLQA","created_at":"2026-07-08T01:19:04.736131+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFMARC4AWLQA6QKQ","created_at":"2026-07-08T01:19:04.736131+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFMARC4A","created_at":"2026-07-08T01:19:04.736131+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5","json":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5.json","graph_json":"https://pith.science/api/pith-number/SFMARC4AWLQA6QKQLPJUYDSFS5/graph.json","events_json":"https://pith.science/api/pith-number/SFMARC4AWLQA6QKQLPJUYDSFS5/events.json","paper":"https://pith.science/paper/SFMARC4A"},"agent_actions":{"view_html":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5","download_json":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5.json","view_paper":"https://pith.science/paper/SFMARC4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.02711&json=true","fetch_graph":"https://pith.science/api/pith-number/SFMARC4AWLQA6QKQLPJUYDSFS5/graph.json","fetch_events":"https://pith.science/api/pith-number/SFMARC4AWLQA6QKQLPJUYDSFS5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5/action/storage_attestation","attest_author":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5/action/author_attestation","sign_citation":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5/action/citation_signature","submit_replication":"https://pith.science/pith/SFMARC4AWLQA6QKQLPJUYDSFS5/action/replication_record"}},"created_at":"2026-07-08T01:19:04.736131+00:00","updated_at":"2026-07-08T01:19:04.736131+00:00"}