{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Y64LHX72IZRAXGJUTBZJCVVICI","short_pith_number":"pith:Y64LHX72","schema_version":"1.0","canonical_sha256":"c7b8b3dffa46620b993498729156a81202f80ca71822cdea2a42ba4eaa94ea2d","source":{"kind":"arxiv","id":"2411.15380","version":1},"attestation_state":"computed","paper":{"title":"Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional Data Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hao Liu","submitted_at":"2024-11-22T23:45:15Z","abstract_excerpt":"Deep learning models often require specially designed architectures to process data of different dimensions, such as 1D time series, 2D images, and 3D volumetric data. Existing bidirectional models mainly focus on sequential data, making it difficult to scale effectively to higher dimensions. To address this issue, we propose a novel multi-dimensional bidirectional neural network architecture, named Nd-BiMamba2, which efficiently handles 1D, 2D, and 3D data. Nd-BiMamba2 is based on the Mamba2 module and introduces innovative bidirectional processing mechanisms and adaptive padding strategies t"},"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":"2411.15380","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-22T23:45:15Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"564ccf5bce8ba209e803a5ddc9c1de231f68ba43bbd8e15cd8a48970531570df","abstract_canon_sha256":"ca56856fe72ad4d0287f1aa6792662383779621c171c15a81c85602683f0d910"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:33.845831Z","signature_b64":"mr8wFa9ZqeoUYm/jSwY9lm8xq9Csi1bUafTYiK3kyvU4C8O2WEgTItwioaeZbHEcfsSNyfdJ2DK50e272gCNBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7b8b3dffa46620b993498729156a81202f80ca71822cdea2a42ba4eaa94ea2d","last_reissued_at":"2026-07-05T09:39:33.845222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:33.845222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional Data Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hao Liu","submitted_at":"2024-11-22T23:45:15Z","abstract_excerpt":"Deep learning models often require specially designed architectures to process data of different dimensions, such as 1D time series, 2D images, and 3D volumetric data. Existing bidirectional models mainly focus on sequential data, making it difficult to scale effectively to higher dimensions. To address this issue, we propose a novel multi-dimensional bidirectional neural network architecture, named Nd-BiMamba2, which efficiently handles 1D, 2D, and 3D data. Nd-BiMamba2 is based on the Mamba2 module and introduces innovative bidirectional processing mechanisms and adaptive padding strategies t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15380","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/2411.15380/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":"2411.15380","created_at":"2026-07-05T09:39:33.845280+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15380v1","created_at":"2026-07-05T09:39:33.845280+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15380","created_at":"2026-07-05T09:39:33.845280+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y64LHX72IZRA","created_at":"2026-07-05T09:39:33.845280+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y64LHX72IZRAXGJU","created_at":"2026-07-05T09:39:33.845280+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y64LHX72","created_at":"2026-07-05T09:39:33.845280+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/Y64LHX72IZRAXGJUTBZJCVVICI","json":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI.json","graph_json":"https://pith.science/api/pith-number/Y64LHX72IZRAXGJUTBZJCVVICI/graph.json","events_json":"https://pith.science/api/pith-number/Y64LHX72IZRAXGJUTBZJCVVICI/events.json","paper":"https://pith.science/paper/Y64LHX72"},"agent_actions":{"view_html":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI","download_json":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI.json","view_paper":"https://pith.science/paper/Y64LHX72","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15380&json=true","fetch_graph":"https://pith.science/api/pith-number/Y64LHX72IZRAXGJUTBZJCVVICI/graph.json","fetch_events":"https://pith.science/api/pith-number/Y64LHX72IZRAXGJUTBZJCVVICI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI/action/storage_attestation","attest_author":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI/action/author_attestation","sign_citation":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI/action/citation_signature","submit_replication":"https://pith.science/pith/Y64LHX72IZRAXGJUTBZJCVVICI/action/replication_record"}},"created_at":"2026-07-05T09:39:33.845280+00:00","updated_at":"2026-07-05T09:39:33.845280+00:00"}