{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:XFQ4JDREMBAZOTV425ZN7GJUF2","short_pith_number":"pith:XFQ4JDRE","schema_version":"1.0","canonical_sha256":"b961c48e246041974ebcd772df99342e8e2eadc70cda83a407f4f293aa7700af","source":{"kind":"arxiv","id":"2506.08049","version":3},"attestation_state":"computed","paper":{"title":"Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Hao Liu, Tengfei Lyu, Weijia Zhang","submitted_at":"2025-06-08T16:32:21Z","abstract_excerpt":"Subseasonal-to-seasonal (S2S) forecasting, which predicts climate conditions from several weeks to months in advance, represents a critical frontier for agricultural planning, energy management, and disaster preparedness. However, it remains one of the most challenging problems in atmospheric science, due to the chaotic dynamics of atmospheric systems and complex interactions across multiple scales. Current approaches often fail to explicitly model underlying physical processes and teleconnections that are crucial at S2S timescales. We introduce \\textbf{TelePiT}, a novel deep learning architec"},"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":"2506.08049","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-08T16:32:21Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c3511177d78b887a52685f8fd30d736a12c686315c71cc2146dfd3fbba8acf56","abstract_canon_sha256":"3b873d11788915deff5e1f03d83e68f5511eb694a57192ac1fb3ac7151bb29ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:51:30.168352Z","signature_b64":"Qhz8q7rox6FyobgP9ExjCZuSvY/g+DoMcuBMtj7yLySinkre2XFUMW9HObjVt6qzI0drEiyQC4C6jTH9mzYJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b961c48e246041974ebcd772df99342e8e2eadc70cda83a407f4f293aa7700af","last_reissued_at":"2026-07-05T11:51:30.167852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:51:30.167852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Hao Liu, Tengfei Lyu, Weijia Zhang","submitted_at":"2025-06-08T16:32:21Z","abstract_excerpt":"Subseasonal-to-seasonal (S2S) forecasting, which predicts climate conditions from several weeks to months in advance, represents a critical frontier for agricultural planning, energy management, and disaster preparedness. However, it remains one of the most challenging problems in atmospheric science, due to the chaotic dynamics of atmospheric systems and complex interactions across multiple scales. Current approaches often fail to explicitly model underlying physical processes and teleconnections that are crucial at S2S timescales. We introduce \\textbf{TelePiT}, a novel deep learning architec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08049","kind":"arxiv","version":3},"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/2506.08049/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":"2506.08049","created_at":"2026-07-05T11:51:30.167910+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.08049v3","created_at":"2026-07-05T11:51:30.167910+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08049","created_at":"2026-07-05T11:51:30.167910+00:00"},{"alias_kind":"pith_short_12","alias_value":"XFQ4JDREMBAZ","created_at":"2026-07-05T11:51:30.167910+00:00"},{"alias_kind":"pith_short_16","alias_value":"XFQ4JDREMBAZOTV4","created_at":"2026-07-05T11:51:30.167910+00:00"},{"alias_kind":"pith_short_8","alias_value":"XFQ4JDRE","created_at":"2026-07-05T11:51:30.167910+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/XFQ4JDREMBAZOTV425ZN7GJUF2","json":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2.json","graph_json":"https://pith.science/api/pith-number/XFQ4JDREMBAZOTV425ZN7GJUF2/graph.json","events_json":"https://pith.science/api/pith-number/XFQ4JDREMBAZOTV425ZN7GJUF2/events.json","paper":"https://pith.science/paper/XFQ4JDRE"},"agent_actions":{"view_html":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2","download_json":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2.json","view_paper":"https://pith.science/paper/XFQ4JDRE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.08049&json=true","fetch_graph":"https://pith.science/api/pith-number/XFQ4JDREMBAZOTV425ZN7GJUF2/graph.json","fetch_events":"https://pith.science/api/pith-number/XFQ4JDREMBAZOTV425ZN7GJUF2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2/action/storage_attestation","attest_author":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2/action/author_attestation","sign_citation":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2/action/citation_signature","submit_replication":"https://pith.science/pith/XFQ4JDREMBAZOTV425ZN7GJUF2/action/replication_record"}},"created_at":"2026-07-05T11:51:30.167910+00:00","updated_at":"2026-07-05T11:51:30.167910+00:00"}