{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WTEUNPMWVCSWQQ7ITNU4F3U67T","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":"1e1e43ab13fca67c29c070491f6ef649295972d01ed8fad72300c3789fa5f282","cross_cats_sorted":["cs.AI","cs.CE","cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T08:45:54Z","title_canon_sha256":"8fcf096e2debb9d1ab0106cf9c06d3046cc31e20a62125108abb5d69d411c280"},"schema_version":"1.0","source":{"id":"2307.09072","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.09072","created_at":"2026-07-05T07:22:01Z"},{"alias_kind":"arxiv_version","alias_value":"2307.09072v2","created_at":"2026-07-05T07:22:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09072","created_at":"2026-07-05T07:22:01Z"},{"alias_kind":"pith_short_12","alias_value":"WTEUNPMWVCSW","created_at":"2026-07-05T07:22:01Z"},{"alias_kind":"pith_short_16","alias_value":"WTEUNPMWVCSWQQ7I","created_at":"2026-07-05T07:22:01Z"},{"alias_kind":"pith_short_8","alias_value":"WTEUNPMW","created_at":"2026-07-05T07:22:01Z"}],"graph_snapshots":[{"event_id":"sha256:7ae7b7bff5e0bda9b78d053007b309f92345964037059f6f4653865bb537ee02","target":"graph","created_at":"2026-07-05T07:22:01Z","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/2307.09072/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Extrapolation remains a grand challenge in deep neural networks across all application domains. We propose an operator learning method to solve time-dependent partial differential equations (PDEs) continuously and with extrapolation in time without any temporal discretization. The proposed method, named Diffusion-inspired Temporal Transformer Operator (DiTTO), is inspired by latent diffusion models and their conditioning mechanism, which we use to incorporate the temporal evolution of the PDE, in combination with elements from the transformer architecture to improve its capabilities. Upon trai","authors_text":"Adar Kahana, Ahmad Peyvan, Eli Turkel, George Em Karniadakis, Oded Ovadia, Vivek Oommen","cross_cats":["cs.AI","cs.CE","cs.NA","math.NA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T08:45:54Z","title":"Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09072","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":"sha256:512b7b371e0e6220e734de3ef7a2b8dabd7226eff37c7dc7a15221a8bfa4cdf5","target":"record","created_at":"2026-07-05T07:22:01Z","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":"1e1e43ab13fca67c29c070491f6ef649295972d01ed8fad72300c3789fa5f282","cross_cats_sorted":["cs.AI","cs.CE","cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-18T08:45:54Z","title_canon_sha256":"8fcf096e2debb9d1ab0106cf9c06d3046cc31e20a62125108abb5d69d411c280"},"schema_version":"1.0","source":{"id":"2307.09072","kind":"arxiv","version":2}},"canonical_sha256":"b4c946bd96a8a56843e89b69c2ee9efce66e9944aa51cfbbb0fc38656af5595b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4c946bd96a8a56843e89b69c2ee9efce66e9944aa51cfbbb0fc38656af5595b","first_computed_at":"2026-07-05T07:22:01.122930Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:22:01.122930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NI9vCpKORcyJ6C/VrjBRZ5YQsl/hMbN8SR6mPqLDAAJiV+cdhlGEoigOpDCXjzRNrX1hEuKX4+r46Nbc30yVBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:22:01.123439Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.09072","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:512b7b371e0e6220e734de3ef7a2b8dabd7226eff37c7dc7a15221a8bfa4cdf5","sha256:7ae7b7bff5e0bda9b78d053007b309f92345964037059f6f4653865bb537ee02"],"state_sha256":"10dd2739e8caa2d0170b5c6ce2be9123e5b123ec7d7606d26c4552648f8316ca"}