{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YDKG43L5GDSP5NMTUZ54IXX6OZ","short_pith_number":"pith:YDKG43L5","canonical_record":{"source":{"id":"2505.02506","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T09:37:58Z","cross_cats_sorted":[],"title_canon_sha256":"f3eda850e55c91bf0e6589bd432b3909136d906cb122c4d29319523a368a3af7","abstract_canon_sha256":"643a05c944960b0811af685a95b8a2d5b50d168909e7d4f1e2576a7202a72627"},"schema_version":"1.0"},"canonical_sha256":"c0d46e6d7d30e4feb593a67bc45efe76466b00fa8490a431f1cf7b24b59f2175","source":{"kind":"arxiv","id":"2505.02506","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.02506","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"arxiv_version","alias_value":"2505.02506v1","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.02506","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"pith_short_12","alias_value":"YDKG43L5GDSP","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"pith_short_16","alias_value":"YDKG43L5GDSP5NMT","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"pith_short_8","alias_value":"YDKG43L5","created_at":"2026-07-05T10:58:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YDKG43L5GDSP5NMTUZ54IXX6OZ","target":"record","payload":{"canonical_record":{"source":{"id":"2505.02506","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T09:37:58Z","cross_cats_sorted":[],"title_canon_sha256":"f3eda850e55c91bf0e6589bd432b3909136d906cb122c4d29319523a368a3af7","abstract_canon_sha256":"643a05c944960b0811af685a95b8a2d5b50d168909e7d4f1e2576a7202a72627"},"schema_version":"1.0"},"canonical_sha256":"c0d46e6d7d30e4feb593a67bc45efe76466b00fa8490a431f1cf7b24b59f2175","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:58:38.355231Z","signature_b64":"s/Sw/espS6Ur4MH1AFhdvZLPI5kqX7eW8LbmhMBM/S6QMHzWHDRr7KSMrJeTFMvLJ6pcpnfEnoW+SqzTrMaiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0d46e6d7d30e4feb593a67bc45efe76466b00fa8490a431f1cf7b24b59f2175","last_reissued_at":"2026-07-05T10:58:38.354705Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:58:38.354705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.02506","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":"2026-07-05T10:58:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qFUHOpe2hHaESpqUVuTb2cd1Itjfu0pXH5ak+itt9OgPqT8teJT4+Pi6DFe4GnxwTqeN/1g7SpOd1Jd3MHFxAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T01:22:00.929800Z"},"content_sha256":"e8aff9a83d2523df3132c741f8c1f96fc2b9556ee146e8e65a9b960749f8ac2f","schema_version":"1.0","event_id":"sha256:e8aff9a83d2523df3132c741f8c1f96fc2b9556ee146e8e65a9b960749f8ac2f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YDKG43L5GDSP5NMTUZ54IXX6OZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploring Design Choices for Autoregressive Deep Learning Climate Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andreas Hotho, Anna Krause, Florian Gallusser, Simon Hentschel","submitted_at":"2025-05-05T09:37:58Z","abstract_excerpt":"Deep Learning models have achieved state-of-the-art performance in medium-range weather prediction but often fail to maintain physically consistent rollouts beyond 14 days. In contrast, a few atmospheric models demonstrate stability over decades, though the key design choices enabling this remain unclear. This study quantitatively compares the long-term stability of three prominent DL-MWP architectures - FourCastNet, SFNO, and ClimaX - trained on ERA5 reanalysis data at 5.625{\\deg} resolution. We systematically assess the impact of autoregressive training steps, model capacity, and choice of p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.02506","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/2505.02506/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":"2026-07-05T10:58:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q/Ya4afl7UHIh7mZRuK2wSyBNi4H9+hGueDXrDPa7oWvcgpVF1bYpVeVLqJbKddq3fMzHRmS1OgC/xm4FuvwDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T01:22:00.930363Z"},"content_sha256":"bbb612bc355ad49b910ec56bbfe930aef1dcf2e53264cac5a4ae8407c3524406","schema_version":"1.0","event_id":"sha256:bbb612bc355ad49b910ec56bbfe930aef1dcf2e53264cac5a4ae8407c3524406"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YDKG43L5GDSP5NMTUZ54IXX6OZ/bundle.json","state_url":"https://pith.science/pith/YDKG43L5GDSP5NMTUZ54IXX6OZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YDKG43L5GDSP5NMTUZ54IXX6OZ/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":"2026-08-19T01:22:00Z","links":{"resolver":"https://pith.science/pith/YDKG43L5GDSP5NMTUZ54IXX6OZ","bundle":"https://pith.science/pith/YDKG43L5GDSP5NMTUZ54IXX6OZ/bundle.json","state":"https://pith.science/pith/YDKG43L5GDSP5NMTUZ54IXX6OZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YDKG43L5GDSP5NMTUZ54IXX6OZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YDKG43L5GDSP5NMTUZ54IXX6OZ","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":"643a05c944960b0811af685a95b8a2d5b50d168909e7d4f1e2576a7202a72627","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T09:37:58Z","title_canon_sha256":"f3eda850e55c91bf0e6589bd432b3909136d906cb122c4d29319523a368a3af7"},"schema_version":"1.0","source":{"id":"2505.02506","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.02506","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"arxiv_version","alias_value":"2505.02506v1","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.02506","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"pith_short_12","alias_value":"YDKG43L5GDSP","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"pith_short_16","alias_value":"YDKG43L5GDSP5NMT","created_at":"2026-07-05T10:58:38Z"},{"alias_kind":"pith_short_8","alias_value":"YDKG43L5","created_at":"2026-07-05T10:58:38Z"}],"graph_snapshots":[{"event_id":"sha256:bbb612bc355ad49b910ec56bbfe930aef1dcf2e53264cac5a4ae8407c3524406","target":"graph","created_at":"2026-07-05T10:58:38Z","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/2505.02506/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Learning models have achieved state-of-the-art performance in medium-range weather prediction but often fail to maintain physically consistent rollouts beyond 14 days. In contrast, a few atmospheric models demonstrate stability over decades, though the key design choices enabling this remain unclear. This study quantitatively compares the long-term stability of three prominent DL-MWP architectures - FourCastNet, SFNO, and ClimaX - trained on ERA5 reanalysis data at 5.625{\\deg} resolution. We systematically assess the impact of autoregressive training steps, model capacity, and choice of p","authors_text":"Andreas Hotho, Anna Krause, Florian Gallusser, Simon Hentschel","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T09:37:58Z","title":"Exploring Design Choices for Autoregressive Deep Learning Climate Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.02506","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:e8aff9a83d2523df3132c741f8c1f96fc2b9556ee146e8e65a9b960749f8ac2f","target":"record","created_at":"2026-07-05T10:58:38Z","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":"643a05c944960b0811af685a95b8a2d5b50d168909e7d4f1e2576a7202a72627","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T09:37:58Z","title_canon_sha256":"f3eda850e55c91bf0e6589bd432b3909136d906cb122c4d29319523a368a3af7"},"schema_version":"1.0","source":{"id":"2505.02506","kind":"arxiv","version":1}},"canonical_sha256":"c0d46e6d7d30e4feb593a67bc45efe76466b00fa8490a431f1cf7b24b59f2175","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c0d46e6d7d30e4feb593a67bc45efe76466b00fa8490a431f1cf7b24b59f2175","first_computed_at":"2026-07-05T10:58:38.354705Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:58:38.354705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s/Sw/espS6Ur4MH1AFhdvZLPI5kqX7eW8LbmhMBM/S6QMHzWHDRr7KSMrJeTFMvLJ6pcpnfEnoW+SqzTrMaiCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:58:38.355231Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.02506","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e8aff9a83d2523df3132c741f8c1f96fc2b9556ee146e8e65a9b960749f8ac2f","sha256:bbb612bc355ad49b910ec56bbfe930aef1dcf2e53264cac5a4ae8407c3524406"],"state_sha256":"f4b65f4f8abbc4f003a7f6fd32c1b32cf6c73331427e7225c7e5cd57948a124b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zS3h26lypN2ZGDFaeXInMSwejDHaK58kL2yGgqacf6sjrb1bFLBY+0Tbw7WrVOyt//EMFYVdv3G6wWVIc1CZBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T01:22:00.935502Z","bundle_sha256":"c8bb539183d89560b32b1f8364b83d7b30016fadeb78d0cbf2a71be774363957"}}