{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:Y5V4I5QAFE34BNFQANLDE5OURX","short_pith_number":"pith:Y5V4I5QA","canonical_record":{"source":{"id":"2506.11328","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-12T21:54:47Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"02956a3e74c639ba6803bc019a94826dd2fffb4a669d47933c455c2ed05d4358","abstract_canon_sha256":"0b06e13d727ed5001286ae11510f82ece5aab4a15392ad48a0bd2b234a47266b"},"schema_version":"1.0"},"canonical_sha256":"c76bc476002937c0b4b003563275d48de20f252c574a393e9b2b8bf3aba59f9e","source":{"kind":"arxiv","id":"2506.11328","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11328","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11328v1","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11328","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_12","alias_value":"Y5V4I5QAFE34","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_16","alias_value":"Y5V4I5QAFE34BNFQ","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_8","alias_value":"Y5V4I5QA","created_at":"2026-07-05T11:21:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:Y5V4I5QAFE34BNFQANLDE5OURX","target":"record","payload":{"canonical_record":{"source":{"id":"2506.11328","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-12T21:54:47Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"02956a3e74c639ba6803bc019a94826dd2fffb4a669d47933c455c2ed05d4358","abstract_canon_sha256":"0b06e13d727ed5001286ae11510f82ece5aab4a15392ad48a0bd2b234a47266b"},"schema_version":"1.0"},"canonical_sha256":"c76bc476002937c0b4b003563275d48de20f252c574a393e9b2b8bf3aba59f9e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:02.602088Z","signature_b64":"R/uJrXqbB70V566V4x/r1AnN0Z58SXIj5oZa4VMcLPnjFYm1ZhLnZB75HqNXSW1UhaOVe01oemQYbHcNKzBtDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c76bc476002937c0b4b003563275d48de20f252c574a393e9b2b8bf3aba59f9e","last_reissued_at":"2026-07-05T11:21:02.601731Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:02.601731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.11328","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:21:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l57G3rv8YHqsTJqAHxNl89JXUMCv20OS+ro6phzc9Nu8orLH/MEoJ8q3aGLRwOOtA8A11sufgOppxsaS+uBpBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:16:52.047341Z"},"content_sha256":"64881c7f21bbea999f97f8ab613bffaa4e7658ec8d72239d0b88a37d495b13cb","schema_version":"1.0","event_id":"sha256:64881c7f21bbea999f97f8ab613bffaa4e7658ec8d72239d0b88a37d495b13cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:Y5V4I5QAFE34BNFQANLDE5OURX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Attention-based Spatio-Temporal Neural Operator for Evolving Physics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Doksoo Lee, Vispi Karkaria, Wei Chen, Yi-Ping Chen, Yue Yu","submitted_at":"2025-06-12T21:54:47Z","abstract_excerpt":"In scientific machine learning (SciML), a key challenge is learning unknown, evolving physical processes and making predictions across spatio-temporal scales. For example, in real-world manufacturing problems like additive manufacturing, users adjust known machine settings while unknown environmental parameters simultaneously fluctuate. To make reliable predictions, it is desired for a model to not only capture long-range spatio-temporal interactions from data but also adapt to new and unknown environments; traditional machine learning models excel at the first task but often lack physical int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11328","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/2506.11328/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:21:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ADK74vwXUYxNk5vXJKllOHplXsPXOGk4+ilSj/F6lxAJvCcfxm0V6Ru+4TqWsMYz/eicYyZK/5235NVkxp9uDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T05:16:52.047861Z"},"content_sha256":"25da218de61b7e5feeb4f333e416614f1bc2b507545a69e0604e0cc696552212","schema_version":"1.0","event_id":"sha256:25da218de61b7e5feeb4f333e416614f1bc2b507545a69e0604e0cc696552212"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y5V4I5QAFE34BNFQANLDE5OURX/bundle.json","state_url":"https://pith.science/pith/Y5V4I5QAFE34BNFQANLDE5OURX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y5V4I5QAFE34BNFQANLDE5OURX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T05:16:52Z","links":{"resolver":"https://pith.science/pith/Y5V4I5QAFE34BNFQANLDE5OURX","bundle":"https://pith.science/pith/Y5V4I5QAFE34BNFQANLDE5OURX/bundle.json","state":"https://pith.science/pith/Y5V4I5QAFE34BNFQANLDE5OURX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y5V4I5QAFE34BNFQANLDE5OURX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Y5V4I5QAFE34BNFQANLDE5OURX","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":"0b06e13d727ed5001286ae11510f82ece5aab4a15392ad48a0bd2b234a47266b","cross_cats_sorted":["cs.CE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-12T21:54:47Z","title_canon_sha256":"02956a3e74c639ba6803bc019a94826dd2fffb4a669d47933c455c2ed05d4358"},"schema_version":"1.0","source":{"id":"2506.11328","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11328","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11328v1","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11328","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_12","alias_value":"Y5V4I5QAFE34","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_16","alias_value":"Y5V4I5QAFE34BNFQ","created_at":"2026-07-05T11:21:02Z"},{"alias_kind":"pith_short_8","alias_value":"Y5V4I5QA","created_at":"2026-07-05T11:21:02Z"}],"graph_snapshots":[{"event_id":"sha256:25da218de61b7e5feeb4f333e416614f1bc2b507545a69e0604e0cc696552212","target":"graph","created_at":"2026-07-05T11:21:02Z","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/2506.11328/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In scientific machine learning (SciML), a key challenge is learning unknown, evolving physical processes and making predictions across spatio-temporal scales. For example, in real-world manufacturing problems like additive manufacturing, users adjust known machine settings while unknown environmental parameters simultaneously fluctuate. To make reliable predictions, it is desired for a model to not only capture long-range spatio-temporal interactions from data but also adapt to new and unknown environments; traditional machine learning models excel at the first task but often lack physical int","authors_text":"Doksoo Lee, Vispi Karkaria, Wei Chen, Yi-Ping Chen, Yue Yu","cross_cats":["cs.CE"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-12T21:54:47Z","title":"An Attention-based Spatio-Temporal Neural Operator for Evolving Physics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11328","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":"sha256:64881c7f21bbea999f97f8ab613bffaa4e7658ec8d72239d0b88a37d495b13cb","target":"record","created_at":"2026-07-05T11:21:02Z","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":"0b06e13d727ed5001286ae11510f82ece5aab4a15392ad48a0bd2b234a47266b","cross_cats_sorted":["cs.CE"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-12T21:54:47Z","title_canon_sha256":"02956a3e74c639ba6803bc019a94826dd2fffb4a669d47933c455c2ed05d4358"},"schema_version":"1.0","source":{"id":"2506.11328","kind":"arxiv","version":1}},"canonical_sha256":"c76bc476002937c0b4b003563275d48de20f252c574a393e9b2b8bf3aba59f9e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c76bc476002937c0b4b003563275d48de20f252c574a393e9b2b8bf3aba59f9e","first_computed_at":"2026-07-05T11:21:02.601731Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:02.601731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R/uJrXqbB70V566V4x/r1AnN0Z58SXIj5oZa4VMcLPnjFYm1ZhLnZB75HqNXSW1UhaOVe01oemQYbHcNKzBtDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:02.602088Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.11328","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:64881c7f21bbea999f97f8ab613bffaa4e7658ec8d72239d0b88a37d495b13cb","sha256:25da218de61b7e5feeb4f333e416614f1bc2b507545a69e0604e0cc696552212"],"state_sha256":"6c3c9eaeecd5b4bcd6d6fe4dc4a91ce7c0d3c3a02bf5691f322cae8be4a6541d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gepsnsPKmUIzDWnf/CWheuP5IAFsH3RzFuxoBWbgaV/mHfMWg1HQ3pisiayBVCTy1xrXjLqkX/BzcA8FncpuCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T05:16:52.051974Z","bundle_sha256":"fb403394b6e9552b1d69e5671a1410e014a2b9e3719032ee34f850f9d61a1281"}}