{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PLT6KBHAMX6NWMKSJO5L7XLRXJ","short_pith_number":"pith:PLT6KBHA","schema_version":"1.0","canonical_sha256":"7ae7e504e065fcdb31524bbabfdd71ba4a56824636622b18af08acf929aefed4","source":{"kind":"arxiv","id":"2411.15241","version":2},"attestation_state":"computed","paper":{"title":"EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hyunwoo J. Kim, Joonmyung Choi, Sanghyeok Lee","submitted_at":"2024-11-22T02:02:06Z","abstract_excerpt":"For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model (SSM) has emerged as an effective operation for global interaction with its favorable linear computational cost in the number of tokens. To harness the efficacy of SSM, we introduce Efficient Vision Mamba (EfficientViM), a novel architecture built on hidden state mixer-based state space duality (HSM-SSD) that efficiently captures global dependencies w"},"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":"2411.15241","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-22T02:02:06Z","cross_cats_sorted":[],"title_canon_sha256":"d18246cca2816f19b53b234d8e136d621d40b43a4d2baa42f07d96e91f6630b4","abstract_canon_sha256":"4c72b3987e9bb19e660cdd915a355174d7b8456a4d1701b5fb4e62fea7029cb9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:38.260490Z","signature_b64":"CbYGTT6YAnvOo8KlB9OQVPYBsKV+lLDu3onyBarhxxSW09Xbikzo3woh7Hv28XEz7ulUH5JByo9J1CmyjDFDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ae7e504e065fcdb31524bbabfdd71ba4a56824636622b18af08acf929aefed4","last_reissued_at":"2026-07-05T10:37:38.259917Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:38.259917Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hyunwoo J. Kim, Joonmyung Choi, Sanghyeok Lee","submitted_at":"2024-11-22T02:02:06Z","abstract_excerpt":"For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model (SSM) has emerged as an effective operation for global interaction with its favorable linear computational cost in the number of tokens. To harness the efficacy of SSM, we introduce Efficient Vision Mamba (EfficientViM), a novel architecture built on hidden state mixer-based state space duality (HSM-SSD) that efficiently captures global dependencies w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.15241","kind":"arxiv","version":2},"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/2411.15241/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":"2411.15241","created_at":"2026-07-05T10:37:38.259981+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.15241v2","created_at":"2026-07-05T10:37:38.259981+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.15241","created_at":"2026-07-05T10:37:38.259981+00:00"},{"alias_kind":"pith_short_12","alias_value":"PLT6KBHAMX6N","created_at":"2026-07-05T10:37:38.259981+00:00"},{"alias_kind":"pith_short_16","alias_value":"PLT6KBHAMX6NWMKS","created_at":"2026-07-05T10:37:38.259981+00:00"},{"alias_kind":"pith_short_8","alias_value":"PLT6KBHA","created_at":"2026-07-05T10:37:38.259981+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.07161","citing_title":"A Survey on Mamba Architecture for Vision Applications","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ","json":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ.json","graph_json":"https://pith.science/api/pith-number/PLT6KBHAMX6NWMKSJO5L7XLRXJ/graph.json","events_json":"https://pith.science/api/pith-number/PLT6KBHAMX6NWMKSJO5L7XLRXJ/events.json","paper":"https://pith.science/paper/PLT6KBHA"},"agent_actions":{"view_html":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ","download_json":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ.json","view_paper":"https://pith.science/paper/PLT6KBHA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.15241&json=true","fetch_graph":"https://pith.science/api/pith-number/PLT6KBHAMX6NWMKSJO5L7XLRXJ/graph.json","fetch_events":"https://pith.science/api/pith-number/PLT6KBHAMX6NWMKSJO5L7XLRXJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ/action/storage_attestation","attest_author":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ/action/author_attestation","sign_citation":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ/action/citation_signature","submit_replication":"https://pith.science/pith/PLT6KBHAMX6NWMKSJO5L7XLRXJ/action/replication_record"}},"created_at":"2026-07-05T10:37:38.259981+00:00","updated_at":"2026-07-05T10:37:38.259981+00:00"}