{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BIN6XPLAXXOWPSCWHCD6APQXQ2","short_pith_number":"pith:BIN6XPLA","schema_version":"1.0","canonical_sha256":"0a1bebbd60bddd67c8563887e03e17868d5a079099a019c122e1d5c161aac447","source":{"kind":"arxiv","id":"2501.12381","version":1},"attestation_state":"computed","paper":{"title":"Parallel Sequence Modeling via Generalized Spatial Propagation Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hongjun Wang, Jan Kautz, Jiarui Xu, Jinwei Gu, Ka Chun Cheung, Kai Han, Sifei Liu, Wonmin Byeon, Xiaolong Wang","submitted_at":"2025-01-21T18:56:19Z","abstract_excerpt":"We present the Generalized Spatial Propagation Network (GSPN), a new attention mechanism optimized for vision tasks that inherently captures 2D spatial structures. Existing attention models, including transformers, linear attention, and state-space models like Mamba, process multi-dimensional data as 1D sequences, compromising spatial coherence and efficiency. GSPN overcomes these limitations by directly operating on spatially coherent image data and forming dense pairwise connections through a line-scan approach. Central to GSPN is the Stability-Context Condition, which ensures stable, contex"},"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":"2501.12381","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-21T18:56:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c50cbc752344b9aad4e98b815c74c88444d3808b273f75f8cb4f15e956446d00","abstract_canon_sha256":"8bdf69eac6f2b8f7b00aa3b426a87d5c8573c9db0651baf1ac23d9aee1c5dc2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:35.339758Z","signature_b64":"+JfULjXI74+7oNSRKl79HLIs+NogcLf7AHj+R8VZFdHjrtsH5TxN4FoMeJXc4B4LzqNjKT9l2FhIH4OhBEt/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a1bebbd60bddd67c8563887e03e17868d5a079099a019c122e1d5c161aac447","last_reissued_at":"2026-07-05T10:03:35.339249Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:35.339249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parallel Sequence Modeling via Generalized Spatial Propagation Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hongjun Wang, Jan Kautz, Jiarui Xu, Jinwei Gu, Ka Chun Cheung, Kai Han, Sifei Liu, Wonmin Byeon, Xiaolong Wang","submitted_at":"2025-01-21T18:56:19Z","abstract_excerpt":"We present the Generalized Spatial Propagation Network (GSPN), a new attention mechanism optimized for vision tasks that inherently captures 2D spatial structures. Existing attention models, including transformers, linear attention, and state-space models like Mamba, process multi-dimensional data as 1D sequences, compromising spatial coherence and efficiency. GSPN overcomes these limitations by directly operating on spatially coherent image data and forming dense pairwise connections through a line-scan approach. Central to GSPN is the Stability-Context Condition, which ensures stable, contex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12381","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/2501.12381/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":"2501.12381","created_at":"2026-07-05T10:03:35.339308+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.12381v1","created_at":"2026-07-05T10:03:35.339308+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12381","created_at":"2026-07-05T10:03:35.339308+00:00"},{"alias_kind":"pith_short_12","alias_value":"BIN6XPLAXXOW","created_at":"2026-07-05T10:03:35.339308+00:00"},{"alias_kind":"pith_short_16","alias_value":"BIN6XPLAXXOWPSCW","created_at":"2026-07-05T10:03:35.339308+00:00"},{"alias_kind":"pith_short_8","alias_value":"BIN6XPLA","created_at":"2026-07-05T10:03:35.339308+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/BIN6XPLAXXOWPSCWHCD6APQXQ2","json":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2.json","graph_json":"https://pith.science/api/pith-number/BIN6XPLAXXOWPSCWHCD6APQXQ2/graph.json","events_json":"https://pith.science/api/pith-number/BIN6XPLAXXOWPSCWHCD6APQXQ2/events.json","paper":"https://pith.science/paper/BIN6XPLA"},"agent_actions":{"view_html":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2","download_json":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2.json","view_paper":"https://pith.science/paper/BIN6XPLA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.12381&json=true","fetch_graph":"https://pith.science/api/pith-number/BIN6XPLAXXOWPSCWHCD6APQXQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/BIN6XPLAXXOWPSCWHCD6APQXQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2/action/storage_attestation","attest_author":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2/action/author_attestation","sign_citation":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2/action/citation_signature","submit_replication":"https://pith.science/pith/BIN6XPLAXXOWPSCWHCD6APQXQ2/action/replication_record"}},"created_at":"2026-07-05T10:03:35.339308+00:00","updated_at":"2026-07-05T10:03:35.339308+00:00"}