{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UE5X6MII2EYPYD4OOFGGYH7R44","short_pith_number":"pith:UE5X6MII","schema_version":"1.0","canonical_sha256":"a13b7f3108d130fc0f8e714c6c1ff1e73b22578f1d673a8db819a4147bd6052d","source":{"kind":"arxiv","id":"2507.12719","version":1},"attestation_state":"computed","paper":{"title":"DPNO: A Dual Path Architecture For Neural Operator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Wenlian Lu, Yichen Wang","submitted_at":"2025-07-17T01:52:18Z","abstract_excerpt":"Neural operators have emerged as a powerful tool for solving partial differential equations (PDEs) and other complex scientific computing tasks. However, the performance of single operator block is often limited, thus often requiring composition of basic operator blocks to achieve better per-formance. The traditional way of composition is staking those blocks like feedforward neural networks, which may not be very economic considering parameter-efficiency tradeoff. In this pa-per, we propose a novel dual path architecture that significantly enhances the capabilities of basic neural operators. "},"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":"2507.12719","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-07-17T01:52:18Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"8dae46166b34e41f1d70083ce857b961848b95dfc64a514343b97e0e0bbd4f61","abstract_canon_sha256":"7a31a530815df163d19ef23c85828c6a999776904ae946b09bda2f9c021721dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:37.849279Z","signature_b64":"k9LeUaf6gxXkYx1qLupkTdqFwWieAKDdz+3tbtjQkg4DR7lVSCyfSASINLA7xvzUlzIEYF+qC16FFeLVTrRQBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a13b7f3108d130fc0f8e714c6c1ff1e73b22578f1d673a8db819a4147bd6052d","last_reissued_at":"2026-07-05T11:38:37.848781Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:37.848781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DPNO: A Dual Path Architecture For Neural Operator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Wenlian Lu, Yichen Wang","submitted_at":"2025-07-17T01:52:18Z","abstract_excerpt":"Neural operators have emerged as a powerful tool for solving partial differential equations (PDEs) and other complex scientific computing tasks. However, the performance of single operator block is often limited, thus often requiring composition of basic operator blocks to achieve better per-formance. The traditional way of composition is staking those blocks like feedforward neural networks, which may not be very economic considering parameter-efficiency tradeoff. In this pa-per, we propose a novel dual path architecture that significantly enhances the capabilities of basic neural operators. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.12719","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/2507.12719/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":"2507.12719","created_at":"2026-07-05T11:38:37.848836+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.12719v1","created_at":"2026-07-05T11:38:37.848836+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.12719","created_at":"2026-07-05T11:38:37.848836+00:00"},{"alias_kind":"pith_short_12","alias_value":"UE5X6MII2EYP","created_at":"2026-07-05T11:38:37.848836+00:00"},{"alias_kind":"pith_short_16","alias_value":"UE5X6MII2EYPYD4O","created_at":"2026-07-05T11:38:37.848836+00:00"},{"alias_kind":"pith_short_8","alias_value":"UE5X6MII","created_at":"2026-07-05T11:38:37.848836+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/UE5X6MII2EYPYD4OOFGGYH7R44","json":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44.json","graph_json":"https://pith.science/api/pith-number/UE5X6MII2EYPYD4OOFGGYH7R44/graph.json","events_json":"https://pith.science/api/pith-number/UE5X6MII2EYPYD4OOFGGYH7R44/events.json","paper":"https://pith.science/paper/UE5X6MII"},"agent_actions":{"view_html":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44","download_json":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44.json","view_paper":"https://pith.science/paper/UE5X6MII","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.12719&json=true","fetch_graph":"https://pith.science/api/pith-number/UE5X6MII2EYPYD4OOFGGYH7R44/graph.json","fetch_events":"https://pith.science/api/pith-number/UE5X6MII2EYPYD4OOFGGYH7R44/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44/action/storage_attestation","attest_author":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44/action/author_attestation","sign_citation":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44/action/citation_signature","submit_replication":"https://pith.science/pith/UE5X6MII2EYPYD4OOFGGYH7R44/action/replication_record"}},"created_at":"2026-07-05T11:38:37.848836+00:00","updated_at":"2026-07-05T11:38:37.848836+00:00"}