{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QNFOHOS7O7QNXXHGNEOO5QR5UL","short_pith_number":"pith:QNFOHOS7","canonical_record":{"source":{"id":"2506.09193","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T19:17:14Z","cross_cats_sorted":[],"title_canon_sha256":"4e870ef3ea7beda04c1cd6ff153434f4af9f8385f7a6e8a1920e8d81089df0c4","abstract_canon_sha256":"94b7e0aa2d4628887ed8fc227688ad7ffa1b5def6fa9c327e093413a37e46530"},"schema_version":"1.0"},"canonical_sha256":"834ae3ba5f77e0dbdce6691ceec23da2edb170d1e841847336d0c41d9bf4461b","source":{"kind":"arxiv","id":"2506.09193","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09193","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09193v1","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09193","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"pith_short_12","alias_value":"QNFOHOS7O7QN","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"pith_short_16","alias_value":"QNFOHOS7O7QNXXHG","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"pith_short_8","alias_value":"QNFOHOS7","created_at":"2026-07-05T11:19:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QNFOHOS7O7QNXXHGNEOO5QR5UL","target":"record","payload":{"canonical_record":{"source":{"id":"2506.09193","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T19:17:14Z","cross_cats_sorted":[],"title_canon_sha256":"4e870ef3ea7beda04c1cd6ff153434f4af9f8385f7a6e8a1920e8d81089df0c4","abstract_canon_sha256":"94b7e0aa2d4628887ed8fc227688ad7ffa1b5def6fa9c327e093413a37e46530"},"schema_version":"1.0"},"canonical_sha256":"834ae3ba5f77e0dbdce6691ceec23da2edb170d1e841847336d0c41d9bf4461b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:32.990815Z","signature_b64":"HhQMi+JltrYGagKVTgt3sbP+0+EMpy0F/4HKxHsUl0X04O+SQn3fWshaqhjAIBk9/FEq9SEVZnc6k+jxdmStAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"834ae3ba5f77e0dbdce6691ceec23da2edb170d1e841847336d0c41d9bf4461b","last_reissued_at":"2026-07-05T11:19:32.990310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:32.990310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.09193","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":"2026-07-05T11:19:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y4FNN+4rDcOZ45+36AbbEFhUV1d6XHYfbcGR0itwsoHgNlcq7zJsYt/nH+UUaYVgVUShuvVB7bU93jtJFhrbDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:12:18.760741Z"},"content_sha256":"be5f4a52932991325d6256e05384f841badd4ae9bbef198a43dff169c63cda86","schema_version":"1.0","event_id":"sha256:be5f4a52932991325d6256e05384f841badd4ae9bbef198a43dff169c63cda86"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QNFOHOS7O7QNXXHGNEOO5QR5UL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Karthik Duraisamy, Yilin Zhuang","submitted_at":"2025-06-10T19:17:14Z","abstract_excerpt":"Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representation, and a transformer-based diffusion model produces sequential latent updates with arbitrary hour "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09193","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/2506.09193/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":"2026-07-05T11:19:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ng7L7KFmME0yEK0FgjosCH4vL+M915xWrWINUC53V6aV0mG4pKxn/VHsQkU0zxNnvKx70cut2a49s8wDMdylBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T23:12:18.761677Z"},"content_sha256":"2a441e1fe63250c78df881b44cd8e58a17e00ed8c8ef9a9a9028e913c89aa536","schema_version":"1.0","event_id":"sha256:2a441e1fe63250c78df881b44cd8e58a17e00ed8c8ef9a9a9028e913c89aa536"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QNFOHOS7O7QNXXHGNEOO5QR5UL/bundle.json","state_url":"https://pith.science/pith/QNFOHOS7O7QNXXHGNEOO5QR5UL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QNFOHOS7O7QNXXHGNEOO5QR5UL/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":"2026-08-18T23:12:18Z","links":{"resolver":"https://pith.science/pith/QNFOHOS7O7QNXXHGNEOO5QR5UL","bundle":"https://pith.science/pith/QNFOHOS7O7QNXXHGNEOO5QR5UL/bundle.json","state":"https://pith.science/pith/QNFOHOS7O7QNXXHGNEOO5QR5UL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QNFOHOS7O7QNXXHGNEOO5QR5UL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QNFOHOS7O7QNXXHGNEOO5QR5UL","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":"94b7e0aa2d4628887ed8fc227688ad7ffa1b5def6fa9c327e093413a37e46530","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T19:17:14Z","title_canon_sha256":"4e870ef3ea7beda04c1cd6ff153434f4af9f8385f7a6e8a1920e8d81089df0c4"},"schema_version":"1.0","source":{"id":"2506.09193","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09193","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09193v1","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09193","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"pith_short_12","alias_value":"QNFOHOS7O7QN","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"pith_short_16","alias_value":"QNFOHOS7O7QNXXHG","created_at":"2026-07-05T11:19:32Z"},{"alias_kind":"pith_short_8","alias_value":"QNFOHOS7","created_at":"2026-07-05T11:19:32Z"}],"graph_snapshots":[{"event_id":"sha256:2a441e1fe63250c78df881b44cd8e58a17e00ed8c8ef9a9a9028e913c89aa536","target":"graph","created_at":"2026-07-05T11:19:32Z","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/2506.09193/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representation, and a transformer-based diffusion model produces sequential latent updates with arbitrary hour ","authors_text":"Karthik Duraisamy, Yilin Zhuang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T19:17:14Z","title":"LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09193","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:be5f4a52932991325d6256e05384f841badd4ae9bbef198a43dff169c63cda86","target":"record","created_at":"2026-07-05T11:19:32Z","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":"94b7e0aa2d4628887ed8fc227688ad7ffa1b5def6fa9c327e093413a37e46530","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-10T19:17:14Z","title_canon_sha256":"4e870ef3ea7beda04c1cd6ff153434f4af9f8385f7a6e8a1920e8d81089df0c4"},"schema_version":"1.0","source":{"id":"2506.09193","kind":"arxiv","version":1}},"canonical_sha256":"834ae3ba5f77e0dbdce6691ceec23da2edb170d1e841847336d0c41d9bf4461b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"834ae3ba5f77e0dbdce6691ceec23da2edb170d1e841847336d0c41d9bf4461b","first_computed_at":"2026-07-05T11:19:32.990310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:32.990310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HhQMi+JltrYGagKVTgt3sbP+0+EMpy0F/4HKxHsUl0X04O+SQn3fWshaqhjAIBk9/FEq9SEVZnc6k+jxdmStAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:32.990815Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.09193","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be5f4a52932991325d6256e05384f841badd4ae9bbef198a43dff169c63cda86","sha256:2a441e1fe63250c78df881b44cd8e58a17e00ed8c8ef9a9a9028e913c89aa536"],"state_sha256":"2c4efeeac49dfb1364c818e55638d14c06bbe5f9d2d4e82bbfbcf22edc2dd102"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cPg17GNl7m2f/ZNjhBRclqP3f03y3wNycO4P2Yp8f6oolWY/9IF8ATxshA37GubW0ZouigORv2sjqW6ysaVhAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T23:12:18.767872Z","bundle_sha256":"4cb3ed909d6e6994f03b154028d843932d6e27f74cd486547e31c14d5fdd7b82"}}