{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PT3Q7PDR4TDIKEWITVCSMEYMN6","short_pith_number":"pith:PT3Q7PDR","schema_version":"1.0","canonical_sha256":"7cf70fbc71e4c68512c89d4526130c6fbb1eff834eb2196b394ce19ac81adcb1","source":{"kind":"arxiv","id":"2606.11162","version":1},"attestation_state":"computed","paper":{"title":"COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Maryam Rahnemoonfar, Zesheng Liu","submitted_at":"2026-06-09T17:43:30Z","abstract_excerpt":"In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT encodes a finite history of system states and associated forcing fields and external forcings with a graph-based history encoder, producing node-wise context vectors that capture both local spatial interactions and temporal evolution. These context vectors initialize and condition a latent Neural Ordinary Differential Equation whose dynamics are driven by interpolated future forcings and explicit relative rollout time"},"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":"2606.11162","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T17:43:30Z","cross_cats_sorted":[],"title_canon_sha256":"bb3aa1e7afad6b4a8fa0df4da38672abdd5bffbf3f205dabd1bd0fb76624318c","abstract_canon_sha256":"f555c4cb6c54605381fe70b7a2470c3fff36a5ff0ef6cbdca6f07ca25d1f0cce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:11:14.635115Z","signature_b64":"AAWw5S0cOlWIrsMM5SKChqL9hA5/YzDUpDdUNbYyq71OF2J/ukN5QtgnGBP2HzyQnpLwLIN0OU4uGLAOqoFlDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cf70fbc71e4c68512c89d4526130c6fbb1eff834eb2196b394ce19ac81adcb1","last_reissued_at":"2026-06-10T01:11:14.634205Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:11:14.634205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Maryam Rahnemoonfar, Zesheng Liu","submitted_at":"2026-06-09T17:43:30Z","abstract_excerpt":"In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT encodes a finite history of system states and associated forcing fields and external forcings with a graph-based history encoder, producing node-wise context vectors that capture both local spatial interactions and temporal evolution. These context vectors initialize and condition a latent Neural Ordinary Differential Equation whose dynamics are driven by interpolated future forcings and explicit relative rollout time"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11162","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/2606.11162/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":"2606.11162","created_at":"2026-06-10T01:11:14.634376+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11162v1","created_at":"2026-06-10T01:11:14.634376+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11162","created_at":"2026-06-10T01:11:14.634376+00:00"},{"alias_kind":"pith_short_12","alias_value":"PT3Q7PDR4TDI","created_at":"2026-06-10T01:11:14.634376+00:00"},{"alias_kind":"pith_short_16","alias_value":"PT3Q7PDR4TDIKEWI","created_at":"2026-06-10T01:11:14.634376+00:00"},{"alias_kind":"pith_short_8","alias_value":"PT3Q7PDR","created_at":"2026-06-10T01:11:14.634376+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/PT3Q7PDR4TDIKEWITVCSMEYMN6","json":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6.json","graph_json":"https://pith.science/api/pith-number/PT3Q7PDR4TDIKEWITVCSMEYMN6/graph.json","events_json":"https://pith.science/api/pith-number/PT3Q7PDR4TDIKEWITVCSMEYMN6/events.json","paper":"https://pith.science/paper/PT3Q7PDR"},"agent_actions":{"view_html":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6","download_json":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6.json","view_paper":"https://pith.science/paper/PT3Q7PDR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11162&json=true","fetch_graph":"https://pith.science/api/pith-number/PT3Q7PDR4TDIKEWITVCSMEYMN6/graph.json","fetch_events":"https://pith.science/api/pith-number/PT3Q7PDR4TDIKEWITVCSMEYMN6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6/action/storage_attestation","attest_author":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6/action/author_attestation","sign_citation":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6/action/citation_signature","submit_replication":"https://pith.science/pith/PT3Q7PDR4TDIKEWITVCSMEYMN6/action/replication_record"}},"created_at":"2026-06-10T01:11:14.634376+00:00","updated_at":"2026-06-10T01:11:14.634376+00:00"}