{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:B244PWIVMNVCIZRSLOCYNBGS2N","short_pith_number":"pith:B244PWIV","schema_version":"1.0","canonical_sha256":"0eb9c7d915636a2466325b858684d2d364ece8677a44961a2f8b132edaf2fcf1","source":{"kind":"arxiv","id":"2607.16251","version":1},"attestation_state":"computed","paper":{"title":"Learning Spatio-Temporal Foundation Models from Pure Synthetic Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fugee Tsung, See-kiong Ng, Shiyuan Piao, Wenqi Fan, Xu Liu, Yutong Feng, Yutong Xia, Yuxuan Liang","submitted_at":"2026-06-27T02:56:46Z","abstract_excerpt":"Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from distributional bias in real-world pre-training data, structural bottlenecks of autoregressive or diffusion-based paradigms, and objectives that overemphasize point-wise reconstruction in noisy observation space.We propose \\textbf{NeoST}, the first spatio-temporal foundation model pre-trained solely on procedurally generated synthetic systems. NeoST introduces a scalable synthetic pre-training corpus to mitigate real-wo"},"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":"2607.16251","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-27T02:56:46Z","cross_cats_sorted":[],"title_canon_sha256":"cbd3c7fa4e8e50dc6e605c1e513786ff9378d5c6be748eba9ba36916987b6e4c","abstract_canon_sha256":"ccd1879784717fd23a0836ff2835f9f1d3758eccb684d9919a20a45642123a1c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T00:20:08.313552Z","signature_b64":"nQUt0FWM4tymsAvnm9PFqaTUoNE8ghWyNhcnFVBUb5VWTb28d3AonDPgyxZlFQe6IbaAB0XkkjrkIlMdA8qMCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0eb9c7d915636a2466325b858684d2d364ece8677a44961a2f8b132edaf2fcf1","last_reissued_at":"2026-07-21T00:20:08.312672Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T00:20:08.312672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Spatio-Temporal Foundation Models from Pure Synthetic Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Fugee Tsung, See-kiong Ng, Shiyuan Piao, Wenqi Fan, Xu Liu, Yutong Feng, Yutong Xia, Yuxuan Liang","submitted_at":"2026-06-27T02:56:46Z","abstract_excerpt":"Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from distributional bias in real-world pre-training data, structural bottlenecks of autoregressive or diffusion-based paradigms, and objectives that overemphasize point-wise reconstruction in noisy observation space.We propose \\textbf{NeoST}, the first spatio-temporal foundation model pre-trained solely on procedurally generated synthetic systems. NeoST introduces a scalable synthetic pre-training corpus to mitigate real-wo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.16251","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/2607.16251/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":"2607.16251","created_at":"2026-07-21T00:20:08.313115+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.16251v1","created_at":"2026-07-21T00:20:08.313115+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.16251","created_at":"2026-07-21T00:20:08.313115+00:00"},{"alias_kind":"pith_short_12","alias_value":"B244PWIVMNVC","created_at":"2026-07-21T00:20:08.313115+00:00"},{"alias_kind":"pith_short_16","alias_value":"B244PWIVMNVCIZRS","created_at":"2026-07-21T00:20:08.313115+00:00"},{"alias_kind":"pith_short_8","alias_value":"B244PWIV","created_at":"2026-07-21T00:20:08.313115+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/B244PWIVMNVCIZRSLOCYNBGS2N","json":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N.json","graph_json":"https://pith.science/api/pith-number/B244PWIVMNVCIZRSLOCYNBGS2N/graph.json","events_json":"https://pith.science/api/pith-number/B244PWIVMNVCIZRSLOCYNBGS2N/events.json","paper":"https://pith.science/paper/B244PWIV"},"agent_actions":{"view_html":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N","download_json":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N.json","view_paper":"https://pith.science/paper/B244PWIV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.16251&json=true","fetch_graph":"https://pith.science/api/pith-number/B244PWIVMNVCIZRSLOCYNBGS2N/graph.json","fetch_events":"https://pith.science/api/pith-number/B244PWIVMNVCIZRSLOCYNBGS2N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N/action/storage_attestation","attest_author":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N/action/author_attestation","sign_citation":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N/action/citation_signature","submit_replication":"https://pith.science/pith/B244PWIVMNVCIZRSLOCYNBGS2N/action/replication_record"}},"created_at":"2026-07-21T00:20:08.313115+00:00","updated_at":"2026-07-21T00:20:08.313115+00:00"}