{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SDHR6CSUXBE26FW3SCCFW6CLNC","short_pith_number":"pith:SDHR6CSU","schema_version":"1.0","canonical_sha256":"90cf1f0a54b849af16db90845b784b68ab09932d22e3410146061e286de368e3","source":{"kind":"arxiv","id":"2605.17403","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Learning for Sparse Matrix Reordering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fangfang Liu, Huiyuan Li, Shuzi Niu, Tao Yuan, Wenjia Wu, Ziwei Li","submitted_at":"2026-05-17T11:54:12Z","abstract_excerpt":"Rearranging the rows or columns of a sparse matrix using an appropriate ordering can significantly reduce fill-ins, i.e., new nonzeros introduced during matrix factorization, decreasing memory usage and runtime. However, finding an ordering that minimizes fill-ins is NP-complete. Existing approaches, including graph-theoretic and deep learning methods, rely on surrogate objectives without theoretical guarantees. The Fill-Path Theorem reveals a direct and intrinsic relationship between fill-in generation and the sparse structure of the matrix as path triplet inequalities. Here we first employ a"},"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":"2605.17403","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T11:54:12Z","cross_cats_sorted":[],"title_canon_sha256":"4842438c0c686eb15539c2572922c6f5ef0730dad600547825a0e79ba46b0dab","abstract_canon_sha256":"381d5eb2f9998335e6224a3d406e3efa4302e43681c43f927eb22490fe65879a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:56.707343Z","signature_b64":"8T3KAvpHPt00qAM7t8yp707udwH4ufmY39R+m+QpZ6NgUE5WzpT550mx5RW7JfQneUn7WHHf8/UYTWRvZO3eCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90cf1f0a54b849af16db90845b784b68ab09932d22e3410146061e286de368e3","last_reissued_at":"2026-05-20T00:03:56.706503Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:56.706503Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Learning for Sparse Matrix Reordering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fangfang Liu, Huiyuan Li, Shuzi Niu, Tao Yuan, Wenjia Wu, Ziwei Li","submitted_at":"2026-05-17T11:54:12Z","abstract_excerpt":"Rearranging the rows or columns of a sparse matrix using an appropriate ordering can significantly reduce fill-ins, i.e., new nonzeros introduced during matrix factorization, decreasing memory usage and runtime. However, finding an ordering that minimizes fill-ins is NP-complete. Existing approaches, including graph-theoretic and deep learning methods, rely on surrogate objectives without theoretical guarantees. The Fill-Path Theorem reveals a direct and intrinsic relationship between fill-in generation and the sparse structure of the matrix as path triplet inequalities. Here we first employ a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17403","kind":"arxiv","version":1},"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/2605.17403/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.752176Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.695257Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"2d20c41972e1c84cb7fa13a9d07ea51fa6bb95c98dd4a2348f2097e94d52b652"},"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":"2605.17403","created_at":"2026-05-20T00:03:56.706633+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17403v1","created_at":"2026-05-20T00:03:56.706633+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17403","created_at":"2026-05-20T00:03:56.706633+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDHR6CSUXBE2","created_at":"2026-05-20T00:03:56.706633+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDHR6CSUXBE26FW3","created_at":"2026-05-20T00:03:56.706633+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDHR6CSU","created_at":"2026-05-20T00:03:56.706633+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/SDHR6CSUXBE26FW3SCCFW6CLNC","json":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC.json","graph_json":"https://pith.science/api/pith-number/SDHR6CSUXBE26FW3SCCFW6CLNC/graph.json","events_json":"https://pith.science/api/pith-number/SDHR6CSUXBE26FW3SCCFW6CLNC/events.json","paper":"https://pith.science/paper/SDHR6CSU"},"agent_actions":{"view_html":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC","download_json":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC.json","view_paper":"https://pith.science/paper/SDHR6CSU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17403&json=true","fetch_graph":"https://pith.science/api/pith-number/SDHR6CSUXBE26FW3SCCFW6CLNC/graph.json","fetch_events":"https://pith.science/api/pith-number/SDHR6CSUXBE26FW3SCCFW6CLNC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC/action/storage_attestation","attest_author":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC/action/author_attestation","sign_citation":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC/action/citation_signature","submit_replication":"https://pith.science/pith/SDHR6CSUXBE26FW3SCCFW6CLNC/action/replication_record"}},"created_at":"2026-05-20T00:03:56.706633+00:00","updated_at":"2026-05-20T00:03:56.706633+00:00"}