{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5O4RPFLG56KZWZTCC7PVPGPCI7","short_pith_number":"pith:5O4RPFLG","canonical_record":{"source":{"id":"2409.02322","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T22:31:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9aeb2536b4bf7c89ad141e3ee7ab3b89bceb0323394be03c186667c6a4c1a08f","abstract_canon_sha256":"e425426d8b8c2f46c9e825d306dae0a5d95c1877fa739d3c1163ec91ca40071f"},"schema_version":"1.0"},"canonical_sha256":"ebb9179566ef959b666217df5799e247e8f0fd6b0702f72d71e2a0d71bfd4bb2","source":{"kind":"arxiv","id":"2409.02322","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02322","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02322v2","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02322","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"pith_short_12","alias_value":"5O4RPFLG56KZ","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"pith_short_16","alias_value":"5O4RPFLG56KZWZTC","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"pith_short_8","alias_value":"5O4RPFLG","created_at":"2026-07-05T10:12:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5O4RPFLG56KZWZTCC7PVPGPCI7","target":"record","payload":{"canonical_record":{"source":{"id":"2409.02322","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T22:31:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9aeb2536b4bf7c89ad141e3ee7ab3b89bceb0323394be03c186667c6a4c1a08f","abstract_canon_sha256":"e425426d8b8c2f46c9e825d306dae0a5d95c1877fa739d3c1163ec91ca40071f"},"schema_version":"1.0"},"canonical_sha256":"ebb9179566ef959b666217df5799e247e8f0fd6b0702f72d71e2a0d71bfd4bb2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:12:19.427849Z","signature_b64":"g+Npx9EfQDbw0lJV3D4tOUfcVwNVWo2U0c4nXr1v2Fx0CUP4NyFF3AgArALxNfJXIIH+FzsDiI7MwQotuQ80AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebb9179566ef959b666217df5799e247e8f0fd6b0702f72d71e2a0d71bfd4bb2","last_reissued_at":"2026-07-05T10:12:19.427313Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:12:19.427313Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.02322","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:12:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TJUSvDe8YTYfz7m4JI0oZGBiAuQBADdbUR/hCGqLpks8/I9McSYt4uLLLHnrzCbJiOlDtzkOzj1QbDHp/mhkBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:46:35.659411Z"},"content_sha256":"b7aac196fadee254dcc1ef044861f0239489876bb1c0d564b62575897e008f5a","schema_version":"1.0","event_id":"sha256:b7aac196fadee254dcc1ef044861f0239489876bb1c0d564b62575897e008f5a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5O4RPFLG56KZWZTCC7PVPGPCI7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Defu Cao, Wen Ye, Yan Liu, Yizhou Zhang","submitted_at":"2024-09-03T22:31:57Z","abstract_excerpt":"Foundation models, particularly Large Language Models (LLMs), have revolutionized text and video processing, yet time series data presents distinct challenges for such approaches due to domain-specific features such as missing values, multi-resolution characteristics, etc. Furthermore, the de-facto autoregressive transformers tend to learn deterministic temporal dependencies within pre-trained data while overlooking inherent uncertainties and lacking integration of physical constraints. In this paper, we introduce TimeDiT, a diffusion transformer model that synergistically combines transformer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02322","kind":"arxiv","version":2},"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/2409.02322/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":"2026-07-05T10:12:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r7JWAMmTEyxsHToDQaO27bUOh0AfDwtG2ec3BwVjOulPGct2UejFlBxtbrkXQMO1v6gIEYCmIldqnm2NA3X4Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:46:35.659912Z"},"content_sha256":"323862b5f3faa43f104b8051cc68a76e9e2a569d14bb919596bb7b31c33b0bb9","schema_version":"1.0","event_id":"sha256:323862b5f3faa43f104b8051cc68a76e9e2a569d14bb919596bb7b31c33b0bb9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/bundle.json","state_url":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/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":"2026-08-06T08:46:35Z","links":{"resolver":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7","bundle":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/bundle.json","state":"https://pith.science/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5O4RPFLG56KZWZTCC7PVPGPCI7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5O4RPFLG56KZWZTCC7PVPGPCI7","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":"e425426d8b8c2f46c9e825d306dae0a5d95c1877fa739d3c1163ec91ca40071f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T22:31:57Z","title_canon_sha256":"9aeb2536b4bf7c89ad141e3ee7ab3b89bceb0323394be03c186667c6a4c1a08f"},"schema_version":"1.0","source":{"id":"2409.02322","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02322","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02322v2","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02322","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"pith_short_12","alias_value":"5O4RPFLG56KZ","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"pith_short_16","alias_value":"5O4RPFLG56KZWZTC","created_at":"2026-07-05T10:12:19Z"},{"alias_kind":"pith_short_8","alias_value":"5O4RPFLG","created_at":"2026-07-05T10:12:19Z"}],"graph_snapshots":[{"event_id":"sha256:323862b5f3faa43f104b8051cc68a76e9e2a569d14bb919596bb7b31c33b0bb9","target":"graph","created_at":"2026-07-05T10:12:19Z","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/2409.02322/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Foundation models, particularly Large Language Models (LLMs), have revolutionized text and video processing, yet time series data presents distinct challenges for such approaches due to domain-specific features such as missing values, multi-resolution characteristics, etc. Furthermore, the de-facto autoregressive transformers tend to learn deterministic temporal dependencies within pre-trained data while overlooking inherent uncertainties and lacking integration of physical constraints. In this paper, we introduce TimeDiT, a diffusion transformer model that synergistically combines transformer","authors_text":"Defu Cao, Wen Ye, Yan Liu, Yizhou Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T22:31:57Z","title":"TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02322","kind":"arxiv","version":2},"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:b7aac196fadee254dcc1ef044861f0239489876bb1c0d564b62575897e008f5a","target":"record","created_at":"2026-07-05T10:12:19Z","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":"e425426d8b8c2f46c9e825d306dae0a5d95c1877fa739d3c1163ec91ca40071f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-09-03T22:31:57Z","title_canon_sha256":"9aeb2536b4bf7c89ad141e3ee7ab3b89bceb0323394be03c186667c6a4c1a08f"},"schema_version":"1.0","source":{"id":"2409.02322","kind":"arxiv","version":2}},"canonical_sha256":"ebb9179566ef959b666217df5799e247e8f0fd6b0702f72d71e2a0d71bfd4bb2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebb9179566ef959b666217df5799e247e8f0fd6b0702f72d71e2a0d71bfd4bb2","first_computed_at":"2026-07-05T10:12:19.427313Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:12:19.427313Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g+Npx9EfQDbw0lJV3D4tOUfcVwNVWo2U0c4nXr1v2Fx0CUP4NyFF3AgArALxNfJXIIH+FzsDiI7MwQotuQ80AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:12:19.427849Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.02322","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b7aac196fadee254dcc1ef044861f0239489876bb1c0d564b62575897e008f5a","sha256:323862b5f3faa43f104b8051cc68a76e9e2a569d14bb919596bb7b31c33b0bb9"],"state_sha256":"7a3d6910eb9b4f89bd59b0c61a850c27f4f5b5757759d8c7bc94c0a59a438f56"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2DbWm1Cx1pBtzYVG+XUQrakrXQykQtJN8ki9c7dkKCpzIEq28cJ0HfXOSKTWZIBSlXso3vAuf7EmQCxNdDvlBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T08:46:35.664772Z","bundle_sha256":"0410e80728fb1b25c6f3632ce5a96c0bb909b18cd70d5a725ceededb3e1851ec"}}