{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WTOZ23RXUNJB4LWSSMQ37NQVNG","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":"7727e74997e34dd62b877684f68e940fefaa1804bdc4c4c541f865b9770ea6dd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-13T11:16:42Z","title_canon_sha256":"80f23c5a5e10770decb99eb94b8f70aba806e4d2466784a9f5ad9b391e6fcc20"},"schema_version":"1.0","source":{"id":"2503.10257","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.10257","created_at":"2026-07-05T10:30:41Z"},{"alias_kind":"arxiv_version","alias_value":"2503.10257v1","created_at":"2026-07-05T10:30:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.10257","created_at":"2026-07-05T10:30:41Z"},{"alias_kind":"pith_short_12","alias_value":"WTOZ23RXUNJB","created_at":"2026-07-05T10:30:41Z"},{"alias_kind":"pith_short_16","alias_value":"WTOZ23RXUNJB4LWS","created_at":"2026-07-05T10:30:41Z"},{"alias_kind":"pith_short_8","alias_value":"WTOZ23RX","created_at":"2026-07-05T10:30:41Z"}],"graph_snapshots":[{"event_id":"sha256:886e6eba9a50658575921d5590a534aa638ba187ae54c16fdada950101277a9e","target":"graph","created_at":"2026-07-05T10:30:41Z","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/2503.10257/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurately and efficiently simulating complex fluid dynamics is a challenging task that has traditionally relied on computationally intensive methods. Neural network-based approaches, such as convolutional and graph neural networks, have partially alleviated this burden by enabling efficient local feature extraction. However, they struggle to capture long-range dependencies due to limited receptive fields, and Transformer-based models, while providing global context, incur prohibitive computational costs. To tackle these challenges, we propose AMR-Transformer, an efficient and accurate neural ","authors_text":"Bingbing Ni, Jinfan Liu, Kuangxu Chen, Ye Chen, Zeyi Xu, Zhangli Hu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-13T11:16:42Z","title":"AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.10257","kind":"arxiv","version":1},"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:c41a743326440d79020e9ad3e56962dbc0cae004a2f77281f9b7a25f325cdb93","target":"record","created_at":"2026-07-05T10:30:41Z","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":"7727e74997e34dd62b877684f68e940fefaa1804bdc4c4c541f865b9770ea6dd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-13T11:16:42Z","title_canon_sha256":"80f23c5a5e10770decb99eb94b8f70aba806e4d2466784a9f5ad9b391e6fcc20"},"schema_version":"1.0","source":{"id":"2503.10257","kind":"arxiv","version":1}},"canonical_sha256":"b4dd9d6e37a3521e2ed29321bfb61569845b3ceacb1eed8036c45cbef1cec01d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4dd9d6e37a3521e2ed29321bfb61569845b3ceacb1eed8036c45cbef1cec01d","first_computed_at":"2026-07-05T10:30:41.524162Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:30:41.524162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aCt96YUXmsiVacuE5iq0DY8zKBD0IgjbIisByCvG5EtEHdJcekiy4XDZx4rSjhckz1+VXvXzG5tImd6zwvuKBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:30:41.524654Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.10257","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c41a743326440d79020e9ad3e56962dbc0cae004a2f77281f9b7a25f325cdb93","sha256:886e6eba9a50658575921d5590a534aa638ba187ae54c16fdada950101277a9e"],"state_sha256":"0eea3519e3cb7f7974b59c191e5d90287b0212d8732b0e5b7a184cbbbd329d1b"}