{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MQFK65VLKWV7RVUCO5EOGTXLQE","short_pith_number":"pith:MQFK65VL","schema_version":"1.0","canonical_sha256":"640aaf76ab55abf8d6827748e34eeb81080c62303000a86f0e6ccd95ba26a65b","source":{"kind":"arxiv","id":"2605.31057","version":1},"attestation_state":"computed","paper":{"title":"LVSA: Training-Free Sparse Attention for Long Video Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Gael Glorian, Hongsheng Liu, Ioannis Lamprou, Yujie Yuan, Zhen Zhang","submitted_at":"2026-05-29T09:28:49Z","abstract_excerpt":"Dense self-attention is the compute and quality bottleneck of long-video diffusion inference: cost grows quadratically with the sequence length, and beyond the training horizon the model converges to near-static output, that is, \"frozen\" repetitive video. State of the art approaches are either too costly, e.g., they require retraining, or fail to satisfy both performance and quality objectives in a scalable manner. To this end, we introduce Long Video Sparse Attention (LVSA), a training-free model-agnostic block-sparse attention for video diffusion transformers that combines a structured windo"},"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":"2605.31057","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T09:28:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dd54eb996c545689f08dc7fb9abcddeeb7738b1adfc8c03ec0a04ab7b0ecc5df","abstract_canon_sha256":"adaf76664d3a74e45137bee690fb917564d61b7fd755744d3da0fd1463e58f3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:33.532044Z","signature_b64":"KXcviSs6BUnKfxgagCSOKUhWxDZv//IsA7CjHgEljMrMQ3/ARQ/l3KQPLKguAAt+dPbcsRpcSolYXDHNfMJ0Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"640aaf76ab55abf8d6827748e34eeb81080c62303000a86f0e6ccd95ba26a65b","last_reissued_at":"2026-06-01T01:03:33.531216Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:33.531216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LVSA: Training-Free Sparse Attention for Long Video Diffusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Gael Glorian, Hongsheng Liu, Ioannis Lamprou, Yujie Yuan, Zhen Zhang","submitted_at":"2026-05-29T09:28:49Z","abstract_excerpt":"Dense self-attention is the compute and quality bottleneck of long-video diffusion inference: cost grows quadratically with the sequence length, and beyond the training horizon the model converges to near-static output, that is, \"frozen\" repetitive video. State of the art approaches are either too costly, e.g., they require retraining, or fail to satisfy both performance and quality objectives in a scalable manner. To this end, we introduce Long Video Sparse Attention (LVSA), a training-free model-agnostic block-sparse attention for video diffusion transformers that combines a structured windo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31057","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/2605.31057/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":"2605.31057","created_at":"2026-06-01T01:03:33.531337+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31057v1","created_at":"2026-06-01T01:03:33.531337+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31057","created_at":"2026-06-01T01:03:33.531337+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQFK65VLKWV7","created_at":"2026-06-01T01:03:33.531337+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQFK65VLKWV7RVUC","created_at":"2026-06-01T01:03:33.531337+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQFK65VL","created_at":"2026-06-01T01:03:33.531337+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/MQFK65VLKWV7RVUCO5EOGTXLQE","json":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE.json","graph_json":"https://pith.science/api/pith-number/MQFK65VLKWV7RVUCO5EOGTXLQE/graph.json","events_json":"https://pith.science/api/pith-number/MQFK65VLKWV7RVUCO5EOGTXLQE/events.json","paper":"https://pith.science/paper/MQFK65VL"},"agent_actions":{"view_html":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE","download_json":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE.json","view_paper":"https://pith.science/paper/MQFK65VL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31057&json=true","fetch_graph":"https://pith.science/api/pith-number/MQFK65VLKWV7RVUCO5EOGTXLQE/graph.json","fetch_events":"https://pith.science/api/pith-number/MQFK65VLKWV7RVUCO5EOGTXLQE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE/action/storage_attestation","attest_author":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE/action/author_attestation","sign_citation":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE/action/citation_signature","submit_replication":"https://pith.science/pith/MQFK65VLKWV7RVUCO5EOGTXLQE/action/replication_record"}},"created_at":"2026-06-01T01:03:33.531337+00:00","updated_at":"2026-06-01T01:03:33.531337+00:00"}