{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZXPWT5DDVU54TZBXZZFFDZ4G2W","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"63b30bb6b39f0ad98d4a273e9962fed55a03df67a134377d4f53212d3a1916e6","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-20T03:48:32Z","title_canon_sha256":"2ea35a93c6e53dd7f39a9a3ac8853fcc3c8256cf3dd3f3d2c81a0bf40fed8f06"},"schema_version":"1.0","source":{"id":"2509.16518","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.16518","created_at":"2026-06-10T00:08:25Z"},{"alias_kind":"arxiv_version","alias_value":"2509.16518v2","created_at":"2026-06-10T00:08:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.16518","created_at":"2026-06-10T00:08:25Z"},{"alias_kind":"pith_short_12","alias_value":"ZXPWT5DDVU54","created_at":"2026-06-10T00:08:25Z"},{"alias_kind":"pith_short_16","alias_value":"ZXPWT5DDVU54TZBX","created_at":"2026-06-10T00:08:25Z"},{"alias_kind":"pith_short_8","alias_value":"ZXPWT5DD","created_at":"2026-06-10T00:08:25Z"}],"graph_snapshots":[{"event_id":"sha256:574ca19eee7eec777e276d9ae93a6f30683d6424c1ef1ef0d4fdf58f4a4609e9","target":"graph","created_at":"2026-06-10T00:08:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.16518/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Using diffusion transformers for media generation may require evaluating attention over extremely long sequences, with attention layers accounting for the majority of generation latency. Exploiting sparsity in attention maps offers a promising opportunity to reduce this cost. In this work, we show that attention maps in diffusion transformers exhibit significant fine-grained sparsity in video generation models. Existing sparse attention methods, however, are too coarse-grained, leaving a large fraction of redundant computation unaddressed, or incur high overheads at finer granularity. We propo","authors_text":"Ashish Gondimalla, Kavya Sreedhar, Nandita Vijaykumar, Narges Shahidi, Sankeerth Durvasula, Suraj Kothawade, Suvinay Subramanian, Tianlei Pang, Zain Moustafa","cross_cats":["cs.AR"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-20T03:48:32Z","title":"FG-Attn: Leveraging Fine-Grained Sparse Attention in Video Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.16518","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2f834c98bd15ea20ce35ec544cb80405aafd06b539a8010b273255a586c67aaf","target":"record","created_at":"2026-06-10T00:08:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"63b30bb6b39f0ad98d4a273e9962fed55a03df67a134377d4f53212d3a1916e6","cross_cats_sorted":["cs.AR"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-20T03:48:32Z","title_canon_sha256":"2ea35a93c6e53dd7f39a9a3ac8853fcc3c8256cf3dd3f3d2c81a0bf40fed8f06"},"schema_version":"1.0","source":{"id":"2509.16518","kind":"arxiv","version":2}},"canonical_sha256":"cddf69f463ad3bc9e437ce4a51e786d59e62152ff04488436de85622fb772d07","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cddf69f463ad3bc9e437ce4a51e786d59e62152ff04488436de85622fb772d07","first_computed_at":"2026-06-10T00:08:25.743179Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T00:08:25.743179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FgO/ljpAag3tiM9Z3kBYG1f8+NycwhwQBhoJznh0XGEuzkYjmcak0U0qzKQpt9svhTJs8GM++Jt2HAl+UMtqDQ==","signature_status":"signed_v1","signed_at":"2026-06-10T00:08:25.744279Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.16518","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f834c98bd15ea20ce35ec544cb80405aafd06b539a8010b273255a586c67aaf","sha256:574ca19eee7eec777e276d9ae93a6f30683d6424c1ef1ef0d4fdf58f4a4609e9"],"state_sha256":"a6d5ee9cf23124876eaaeaec8d6322afc927326ba346e21a82bdd992ba0bdb26"}