{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:2S4IUEBMNX2BVFLWSUYVHBABF3","short_pith_number":"pith:2S4IUEBM","canonical_record":{"source":{"id":"2602.17706","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-10T14:31:53Z","cross_cats_sorted":[],"title_canon_sha256":"678eaf1731888feb57acb5f0c46c52073e8754648f2fe637a7660ff6ab86b8d3","abstract_canon_sha256":"b5090d0a7933944ca2d1f63bb181fb0b92bdcad331378d15388ad0a13e3ddb5a"},"schema_version":"1.0"},"canonical_sha256":"d4b88a102c6df41a957695315384012ece5268dc31a5f7484c4827dd85f67f4b","source":{"kind":"arxiv","id":"2602.17706","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.17706","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"arxiv_version","alias_value":"2602.17706v2","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.17706","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"pith_short_12","alias_value":"2S4IUEBMNX2B","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"pith_short_16","alias_value":"2S4IUEBMNX2BVFLW","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"pith_short_8","alias_value":"2S4IUEBM","created_at":"2026-06-02T02:04:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:2S4IUEBMNX2BVFLWSUYVHBABF3","target":"record","payload":{"canonical_record":{"source":{"id":"2602.17706","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-10T14:31:53Z","cross_cats_sorted":[],"title_canon_sha256":"678eaf1731888feb57acb5f0c46c52073e8754648f2fe637a7660ff6ab86b8d3","abstract_canon_sha256":"b5090d0a7933944ca2d1f63bb181fb0b92bdcad331378d15388ad0a13e3ddb5a"},"schema_version":"1.0"},"canonical_sha256":"d4b88a102c6df41a957695315384012ece5268dc31a5f7484c4827dd85f67f4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:15.089926Z","signature_b64":"jRUYlOuDhfh3JEMVMawzoJXy5iUymenRl1d5tZ4fInrKB4fKml8etUhukUsS70Rd+TlxDzlUVG7QtCHxwgyjCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4b88a102c6df41a957695315384012ece5268dc31a5f7484c4827dd85f67f4b","last_reissued_at":"2026-06-02T02:04:15.089416Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:15.089416Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.17706","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":"2026-06-02T02:04:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cseY+7rzTg/t2JWKnIPPw4lbqou1vRVAM2a2QaF505oxIqiDyYrPm1oSDCZ/3eEI4K9130oS5mOBMoWD42utBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:56:35.826008Z"},"content_sha256":"b14cefe4a33194f3deb914fc20bee60fc85b83b8eca49f2683d1652b1e6623b7","schema_version":"1.0","event_id":"sha256:b14cefe4a33194f3deb914fc20bee60fc85b83b8eca49f2683d1652b1e6623b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:2S4IUEBMNX2BVFLWSUYVHBABF3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Parallel Complex Diffusion for Scalable Time Series Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Kexin Zhang, Ming Jin, Qingsong Wen, Rongyao Cai, Yong Liu, Yuxi Wan, Zhiqiang Ge","submitted_at":"2026-02-10T14:31:53Z","abstract_excerpt":"Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time series, strong temporal dependence forces the noise / score estimator to recover highly entangled cross-time relationships, leading to the curse of entanglement. We mitigate this burden by changing the topology of the diffusion space: the Discrete Fourier Transform (DFT) decomposes temporal dependencies into spectral modes, diagonalizing second-order dependency structure and better aligning the data manifold with isotrop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.17706","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/2602.17706/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":"2026-06-02T02:04:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kA1xqb0+2KlSWM6lQ59uvOvv4pctBR/telLVIa8LEfEIKMtkYWpS3zmUrm2tXcxD5nJmvfGlivq0tADcLsmMBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:56:35.826904Z"},"content_sha256":"15d2c5d373dff566cff4f2cf0064851449c7aabeb36dc5f86c2bbf4540ff52f6","schema_version":"1.0","event_id":"sha256:15d2c5d373dff566cff4f2cf0064851449c7aabeb36dc5f86c2bbf4540ff52f6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2S4IUEBMNX2BVFLWSUYVHBABF3/bundle.json","state_url":"https://pith.science/pith/2S4IUEBMNX2BVFLWSUYVHBABF3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2S4IUEBMNX2BVFLWSUYVHBABF3/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":"2026-06-11T22:56:35Z","links":{"resolver":"https://pith.science/pith/2S4IUEBMNX2BVFLWSUYVHBABF3","bundle":"https://pith.science/pith/2S4IUEBMNX2BVFLWSUYVHBABF3/bundle.json","state":"https://pith.science/pith/2S4IUEBMNX2BVFLWSUYVHBABF3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2S4IUEBMNX2BVFLWSUYVHBABF3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2S4IUEBMNX2BVFLWSUYVHBABF3","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":"b5090d0a7933944ca2d1f63bb181fb0b92bdcad331378d15388ad0a13e3ddb5a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-10T14:31:53Z","title_canon_sha256":"678eaf1731888feb57acb5f0c46c52073e8754648f2fe637a7660ff6ab86b8d3"},"schema_version":"1.0","source":{"id":"2602.17706","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.17706","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"arxiv_version","alias_value":"2602.17706v2","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.17706","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"pith_short_12","alias_value":"2S4IUEBMNX2B","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"pith_short_16","alias_value":"2S4IUEBMNX2BVFLW","created_at":"2026-06-02T02:04:15Z"},{"alias_kind":"pith_short_8","alias_value":"2S4IUEBM","created_at":"2026-06-02T02:04:15Z"}],"graph_snapshots":[{"event_id":"sha256:15d2c5d373dff566cff4f2cf0064851449c7aabeb36dc5f86c2bbf4540ff52f6","target":"graph","created_at":"2026-06-02T02:04:15Z","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/2602.17706/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time series, strong temporal dependence forces the noise / score estimator to recover highly entangled cross-time relationships, leading to the curse of entanglement. We mitigate this burden by changing the topology of the diffusion space: the Discrete Fourier Transform (DFT) decomposes temporal dependencies into spectral modes, diagonalizing second-order dependency structure and better aligning the data manifold with isotrop","authors_text":"Kexin Zhang, Ming Jin, Qingsong Wen, Rongyao Cai, Yong Liu, Yuxi Wan, Zhiqiang Ge","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-10T14:31:53Z","title":"Parallel Complex Diffusion for Scalable Time Series Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.17706","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:b14cefe4a33194f3deb914fc20bee60fc85b83b8eca49f2683d1652b1e6623b7","target":"record","created_at":"2026-06-02T02:04:15Z","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":"b5090d0a7933944ca2d1f63bb181fb0b92bdcad331378d15388ad0a13e3ddb5a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-10T14:31:53Z","title_canon_sha256":"678eaf1731888feb57acb5f0c46c52073e8754648f2fe637a7660ff6ab86b8d3"},"schema_version":"1.0","source":{"id":"2602.17706","kind":"arxiv","version":2}},"canonical_sha256":"d4b88a102c6df41a957695315384012ece5268dc31a5f7484c4827dd85f67f4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d4b88a102c6df41a957695315384012ece5268dc31a5f7484c4827dd85f67f4b","first_computed_at":"2026-06-02T02:04:15.089416Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:04:15.089416Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jRUYlOuDhfh3JEMVMawzoJXy5iUymenRl1d5tZ4fInrKB4fKml8etUhukUsS70Rd+TlxDzlUVG7QtCHxwgyjCQ==","signature_status":"signed_v1","signed_at":"2026-06-02T02:04:15.089926Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.17706","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b14cefe4a33194f3deb914fc20bee60fc85b83b8eca49f2683d1652b1e6623b7","sha256:15d2c5d373dff566cff4f2cf0064851449c7aabeb36dc5f86c2bbf4540ff52f6"],"state_sha256":"0b4781c7502ba191a44a19b0521d2f8be6dce3172b3ceb42f96ad441991c8b9a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HNf5Nv6EI57auF1ublhUuUKbPm8DVLf1VGR6R5Mqi+lqMfUx7PfeoM4cQgep1QiMqGcS7cqOUDx26X+IrsdtDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T22:56:35.830969Z","bundle_sha256":"bc9968fbbf49a3d89e05a7d3a65853d0b2b8a98dafc82030a258f8c35a75c988"}}