{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AJPFGAKKU6OONFCUUMMS3FO2BT","short_pith_number":"pith:AJPFGAKK","schema_version":"1.0","canonical_sha256":"025e53014aa79ce69454a3192d95da0cf26f7457455df429af09cf737ae7401a","source":{"kind":"arxiv","id":"2508.01748","version":1},"attestation_state":"computed","paper":{"title":"Towards Faster Feasible Matrix Multiplication by Trilinear Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Eyal Zwecher, Oded Schwartz","submitted_at":"2025-08-03T13:15:24Z","abstract_excerpt":"Matrix multiplication is a fundamental kernel in high performance computing. Many algorithms for fast matrix multiplication can only be applied to enormous matrices ($n>10^{100}$) and thus cannot be used in practice. Of all algorithms applicable to feasible input, Pan's $O(n^{2.773372})$ algorithm (1982) is asymptotically the fastest. We obtain an $O(n^{2.773203})$ algorithm applicable to the same input sizes as Pan's algorithm. This algorithm is the fastest matrix multiplication algorithm with base case smaller than $1000$. Further, our method obtains the best asymptotic complexity for many s"},"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":"2508.01748","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2025-08-03T13:15:24Z","cross_cats_sorted":[],"title_canon_sha256":"2fb62b1c2623d70b1f6dd3202090b0cf0bde876ce53f2c09a8da6779d6ca2979","abstract_canon_sha256":"73c1dfa8dd05b75cdd4f513e250886946dc8df7a2db16f8de6c2fcc7ca99e39e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:44.684415Z","signature_b64":"UUp45Fq7NUze5LVF65z+ixncLghrbsKtaGO3LU5VqQ1XjNGro7Iv8zNc7LLjYkiLw7zk4xwPqS6ZZvObvEd/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"025e53014aa79ce69454a3192d95da0cf26f7457455df429af09cf737ae7401a","last_reissued_at":"2026-07-05T11:47:44.683852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:44.683852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Faster Feasible Matrix Multiplication by Trilinear Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Eyal Zwecher, Oded Schwartz","submitted_at":"2025-08-03T13:15:24Z","abstract_excerpt":"Matrix multiplication is a fundamental kernel in high performance computing. Many algorithms for fast matrix multiplication can only be applied to enormous matrices ($n>10^{100}$) and thus cannot be used in practice. Of all algorithms applicable to feasible input, Pan's $O(n^{2.773372})$ algorithm (1982) is asymptotically the fastest. We obtain an $O(n^{2.773203})$ algorithm applicable to the same input sizes as Pan's algorithm. This algorithm is the fastest matrix multiplication algorithm with base case smaller than $1000$. Further, our method obtains the best asymptotic complexity for many s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.01748","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/2508.01748/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":"2508.01748","created_at":"2026-07-05T11:47:44.683923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.01748v1","created_at":"2026-07-05T11:47:44.683923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.01748","created_at":"2026-07-05T11:47:44.683923+00:00"},{"alias_kind":"pith_short_12","alias_value":"AJPFGAKKU6OO","created_at":"2026-07-05T11:47:44.683923+00:00"},{"alias_kind":"pith_short_16","alias_value":"AJPFGAKKU6OONFCU","created_at":"2026-07-05T11:47:44.683923+00:00"},{"alias_kind":"pith_short_8","alias_value":"AJPFGAKK","created_at":"2026-07-05T11:47:44.683923+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.11041","citing_title":"Exploiting the Structure in Tensor Decompositions for Matrix Multiplication","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT","json":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT.json","graph_json":"https://pith.science/api/pith-number/AJPFGAKKU6OONFCUUMMS3FO2BT/graph.json","events_json":"https://pith.science/api/pith-number/AJPFGAKKU6OONFCUUMMS3FO2BT/events.json","paper":"https://pith.science/paper/AJPFGAKK"},"agent_actions":{"view_html":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT","download_json":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT.json","view_paper":"https://pith.science/paper/AJPFGAKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.01748&json=true","fetch_graph":"https://pith.science/api/pith-number/AJPFGAKKU6OONFCUUMMS3FO2BT/graph.json","fetch_events":"https://pith.science/api/pith-number/AJPFGAKKU6OONFCUUMMS3FO2BT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT/action/storage_attestation","attest_author":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT/action/author_attestation","sign_citation":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT/action/citation_signature","submit_replication":"https://pith.science/pith/AJPFGAKKU6OONFCUUMMS3FO2BT/action/replication_record"}},"created_at":"2026-07-05T11:47:44.683923+00:00","updated_at":"2026-07-05T11:47:44.683923+00:00"}