{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:25ZCC6AXJEDDLK3X2PBUZZFPFQ","short_pith_number":"pith:25ZCC6AX","canonical_record":{"source":{"id":"2507.02939","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-27T14:24:37Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"42c056e568620d07224ddd0a638ca1177ce62fd759426c5c64f444829b92ea7a","abstract_canon_sha256":"60e25bbf16d3eb16c3b554bcac7794afa502fc5d1be8ca3a141331a9c63f790b"},"schema_version":"1.0"},"canonical_sha256":"d772217817490635ab77d3c34ce4af2c2ea2e72e924440dc81cec7cdbbc677f5","source":{"kind":"arxiv","id":"2507.02939","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.02939","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"arxiv_version","alias_value":"2507.02939v2","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02939","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"pith_short_12","alias_value":"25ZCC6AXJEDD","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"pith_short_16","alias_value":"25ZCC6AXJEDDLK3X","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"pith_short_8","alias_value":"25ZCC6AX","created_at":"2026-07-05T11:39:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:25ZCC6AXJEDDLK3X2PBUZZFPFQ","target":"record","payload":{"canonical_record":{"source":{"id":"2507.02939","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-27T14:24:37Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"42c056e568620d07224ddd0a638ca1177ce62fd759426c5c64f444829b92ea7a","abstract_canon_sha256":"60e25bbf16d3eb16c3b554bcac7794afa502fc5d1be8ca3a141331a9c63f790b"},"schema_version":"1.0"},"canonical_sha256":"d772217817490635ab77d3c34ce4af2c2ea2e72e924440dc81cec7cdbbc677f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:55.951241Z","signature_b64":"KRZRpdtYht8H9qsU5BZkph2TTuE69GcOpKa24IWdVkRU9rN6z6dXvq8m32DCEo4Eiptqcr2Dfq9sy+c1JZh0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d772217817490635ab77d3c34ce4af2c2ea2e72e924440dc81cec7cdbbc677f5","last_reissued_at":"2026-07-05T11:39:55.950821Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:55.950821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.02939","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":"2026-07-05T11:39:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lr0LjUMareNZKX4y5AuEcHv8ikVtHE6anbKHoJFRda7f0lYRuhIrfel3RuGUuc7eqofVeoiRjIA0GCvtBbskCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T03:51:37.597790Z"},"content_sha256":"bfd9bf452ee37382b50faa5e1f8729b538b7b33acba097951525755da11732fe","schema_version":"1.0","event_id":"sha256:bfd9bf452ee37382b50faa5e1f8729b538b7b33acba097951525755da11732fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:25ZCC6AXJEDDLK3X2PBUZZFPFQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Chuanguang Yang, Hansheng Zeng, Hao Wu, Yingli Tian, Yongjun Xu, Yuqi Li, Zeyu Dong, Zhulin An","submitted_at":"2025-06-27T14:24:37Z","abstract_excerpt":"Spatiotemporal forecasting tasks, such as traffic flow, combustion dynamics, and weather forecasting, often require complex models that suffer from low training efficiency and high memory consumption. This paper proposes a lightweight framework, Spectral Decoupled Knowledge Distillation (termed SDKD), which transfers the multi-scale spatiotemporal representations from a complex teacher model to a more efficient lightweight student network. The teacher model follows an encoder-latent evolution-decoder architecture, where its latent evolution module decouples high-frequency details and low-frequ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02939","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/2507.02939/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":"2026-07-05T11:39:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/KRtuq/K4Ck+vU29f1yMtmttcdkOHhce57kft3d3YomoP3+Z76dLGwUtv3ZAKN9Tjv0HyGbhm4wcglz+R5+bAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T03:51:37.598271Z"},"content_sha256":"3c83c052751dd667593995a0e188e3f25b3d3d5151580086bac55ed5330187e0","schema_version":"1.0","event_id":"sha256:3c83c052751dd667593995a0e188e3f25b3d3d5151580086bac55ed5330187e0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/25ZCC6AXJEDDLK3X2PBUZZFPFQ/bundle.json","state_url":"https://pith.science/pith/25ZCC6AXJEDDLK3X2PBUZZFPFQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/25ZCC6AXJEDDLK3X2PBUZZFPFQ/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":"2026-08-03T03:51:37Z","links":{"resolver":"https://pith.science/pith/25ZCC6AXJEDDLK3X2PBUZZFPFQ","bundle":"https://pith.science/pith/25ZCC6AXJEDDLK3X2PBUZZFPFQ/bundle.json","state":"https://pith.science/pith/25ZCC6AXJEDDLK3X2PBUZZFPFQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/25ZCC6AXJEDDLK3X2PBUZZFPFQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:25ZCC6AXJEDDLK3X2PBUZZFPFQ","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":"60e25bbf16d3eb16c3b554bcac7794afa502fc5d1be8ca3a141331a9c63f790b","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-27T14:24:37Z","title_canon_sha256":"42c056e568620d07224ddd0a638ca1177ce62fd759426c5c64f444829b92ea7a"},"schema_version":"1.0","source":{"id":"2507.02939","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.02939","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"arxiv_version","alias_value":"2507.02939v2","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02939","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"pith_short_12","alias_value":"25ZCC6AXJEDD","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"pith_short_16","alias_value":"25ZCC6AXJEDDLK3X","created_at":"2026-07-05T11:39:55Z"},{"alias_kind":"pith_short_8","alias_value":"25ZCC6AX","created_at":"2026-07-05T11:39:55Z"}],"graph_snapshots":[{"event_id":"sha256:3c83c052751dd667593995a0e188e3f25b3d3d5151580086bac55ed5330187e0","target":"graph","created_at":"2026-07-05T11:39:55Z","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/2507.02939/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatiotemporal forecasting tasks, such as traffic flow, combustion dynamics, and weather forecasting, often require complex models that suffer from low training efficiency and high memory consumption. This paper proposes a lightweight framework, Spectral Decoupled Knowledge Distillation (termed SDKD), which transfers the multi-scale spatiotemporal representations from a complex teacher model to a more efficient lightweight student network. The teacher model follows an encoder-latent evolution-decoder architecture, where its latent evolution module decouples high-frequency details and low-frequ","authors_text":"Chuanguang Yang, Hansheng Zeng, Hao Wu, Yingli Tian, Yongjun Xu, Yuqi Li, Zeyu Dong, Zhulin An","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-27T14:24:37Z","title":"Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02939","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:bfd9bf452ee37382b50faa5e1f8729b538b7b33acba097951525755da11732fe","target":"record","created_at":"2026-07-05T11:39:55Z","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":"60e25bbf16d3eb16c3b554bcac7794afa502fc5d1be8ca3a141331a9c63f790b","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-27T14:24:37Z","title_canon_sha256":"42c056e568620d07224ddd0a638ca1177ce62fd759426c5c64f444829b92ea7a"},"schema_version":"1.0","source":{"id":"2507.02939","kind":"arxiv","version":2}},"canonical_sha256":"d772217817490635ab77d3c34ce4af2c2ea2e72e924440dc81cec7cdbbc677f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d772217817490635ab77d3c34ce4af2c2ea2e72e924440dc81cec7cdbbc677f5","first_computed_at":"2026-07-05T11:39:55.950821Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:39:55.950821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KRZRpdtYht8H9qsU5BZkph2TTuE69GcOpKa24IWdVkRU9rN6z6dXvq8m32DCEo4Eiptqcr2Dfq9sy+c1JZh0BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:39:55.951241Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.02939","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bfd9bf452ee37382b50faa5e1f8729b538b7b33acba097951525755da11732fe","sha256:3c83c052751dd667593995a0e188e3f25b3d3d5151580086bac55ed5330187e0"],"state_sha256":"86db9559fa1695f647dbb37c6b02afb8295518ce349f67d7d06b57381d4b04e9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GkS01fJS/nKACIkVnUK6U3ajMfVFs6bEdHpzBS/ufPNGtBNQBhps0qZ4W78pyoMJWyBG0GjkqzZLjfOPMf8tDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T03:51:37.601935Z","bundle_sha256":"14774a9e5da9818efa9eef6d932ee6c0c578d71c23726e93d9ece24b89d78fce"}}