{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:LCCK5P264ZMZXGTMP2SSNCVFFM","short_pith_number":"pith:LCCK5P26","canonical_record":{"source":{"id":"2406.11921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T07:36:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"35f6194006be23fdd201a7649e84f2da726dc4a46cbfb49b7f9d7c37b998c626","abstract_canon_sha256":"53095e4736361856856e256c9be01489763e7f80a516c3845605a8106d5ab42f"},"schema_version":"1.0"},"canonical_sha256":"5884aebf5ee6599b9a6c7ea5268aa52b0536dbb9e00739bc32d8dc5feb6c81b6","source":{"kind":"arxiv","id":"2406.11921","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11921","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11921v1","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11921","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"pith_short_12","alias_value":"LCCK5P264ZMZ","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"pith_short_16","alias_value":"LCCK5P264ZMZXGTM","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"pith_short_8","alias_value":"LCCK5P26","created_at":"2026-07-05T08:33:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:LCCK5P264ZMZXGTMP2SSNCVFFM","target":"record","payload":{"canonical_record":{"source":{"id":"2406.11921","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T07:36:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"35f6194006be23fdd201a7649e84f2da726dc4a46cbfb49b7f9d7c37b998c626","abstract_canon_sha256":"53095e4736361856856e256c9be01489763e7f80a516c3845605a8106d5ab42f"},"schema_version":"1.0"},"canonical_sha256":"5884aebf5ee6599b9a6c7ea5268aa52b0536dbb9e00739bc32d8dc5feb6c81b6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:33:03.613450Z","signature_b64":"hlfebWI655ZVAhs73FGdRqPh1574pg3HzFY9q1E+eThXx+g8wSlHvk07F6H43Yi67e+5eGHHgO2iyYYli2WvDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5884aebf5ee6599b9a6c7ea5268aa52b0536dbb9e00739bc32d8dc5feb6c81b6","last_reissued_at":"2026-07-05T08:33:03.613021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:33:03.613021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.11921","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":"2026-07-05T08:33:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"geIv64qhSRe1W3gMzB8FAGqiZ3PZ58v5eypqzEFlBQxJGb+6+rtoGKvdj7i6WE8Dmufrx2ZTjnHQLr/zWjvnDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T12:04:52.383267Z"},"content_sha256":"fb226f495df8e6477a75590baca9722872efbff7c2b7fbd48f99745261b51cc2","schema_version":"1.0","event_id":"sha256:fb226f495df8e6477a75590baca9722872efbff7c2b7fbd48f99745261b51cc2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:LCCK5P264ZMZXGTMP2SSNCVFFM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiaqi Lin, Qianqian Ren","submitted_at":"2024-06-17T07:36:57Z","abstract_excerpt":"Traffic prediction is a challenging spatio-temporal forecasting problem that involves highly complex spatio-temporal correlations. This paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction. The model aims to capture spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from the perspectives of local, global, and pivotal nodes. By combining "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11921","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/2406.11921/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":"2026-07-05T08:33:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RqBWaLknRlBX7aRmIyXpJyjR0Dkfyy9/6sfLtDOzbHwjISl2wDM7zFo1jMiCrfuA11H9RLbTZXSbmfO/RQjIBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T12:04:52.384147Z"},"content_sha256":"4d2ba9db62bb4cb975caa3c3602a5c75cb4238dd0ed66199a0d6545a51960e0c","schema_version":"1.0","event_id":"sha256:4d2ba9db62bb4cb975caa3c3602a5c75cb4238dd0ed66199a0d6545a51960e0c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LCCK5P264ZMZXGTMP2SSNCVFFM/bundle.json","state_url":"https://pith.science/pith/LCCK5P264ZMZXGTMP2SSNCVFFM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LCCK5P264ZMZXGTMP2SSNCVFFM/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":"2026-08-20T12:04:52Z","links":{"resolver":"https://pith.science/pith/LCCK5P264ZMZXGTMP2SSNCVFFM","bundle":"https://pith.science/pith/LCCK5P264ZMZXGTMP2SSNCVFFM/bundle.json","state":"https://pith.science/pith/LCCK5P264ZMZXGTMP2SSNCVFFM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LCCK5P264ZMZXGTMP2SSNCVFFM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LCCK5P264ZMZXGTMP2SSNCVFFM","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":"53095e4736361856856e256c9be01489763e7f80a516c3845605a8106d5ab42f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T07:36:57Z","title_canon_sha256":"35f6194006be23fdd201a7649e84f2da726dc4a46cbfb49b7f9d7c37b998c626"},"schema_version":"1.0","source":{"id":"2406.11921","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.11921","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"arxiv_version","alias_value":"2406.11921v1","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11921","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"pith_short_12","alias_value":"LCCK5P264ZMZ","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"pith_short_16","alias_value":"LCCK5P264ZMZXGTM","created_at":"2026-07-05T08:33:03Z"},{"alias_kind":"pith_short_8","alias_value":"LCCK5P26","created_at":"2026-07-05T08:33:03Z"}],"graph_snapshots":[{"event_id":"sha256:4d2ba9db62bb4cb975caa3c3602a5c75cb4238dd0ed66199a0d6545a51960e0c","target":"graph","created_at":"2026-07-05T08:33:03Z","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/2406.11921/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traffic prediction is a challenging spatio-temporal forecasting problem that involves highly complex spatio-temporal correlations. This paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction. The model aims to capture spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from the perspectives of local, global, and pivotal nodes. By combining ","authors_text":"Jiaqi Lin, Qianqian Ren","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T07:36:57Z","title":"Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11921","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:fb226f495df8e6477a75590baca9722872efbff7c2b7fbd48f99745261b51cc2","target":"record","created_at":"2026-07-05T08:33:03Z","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":"53095e4736361856856e256c9be01489763e7f80a516c3845605a8106d5ab42f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-17T07:36:57Z","title_canon_sha256":"35f6194006be23fdd201a7649e84f2da726dc4a46cbfb49b7f9d7c37b998c626"},"schema_version":"1.0","source":{"id":"2406.11921","kind":"arxiv","version":1}},"canonical_sha256":"5884aebf5ee6599b9a6c7ea5268aa52b0536dbb9e00739bc32d8dc5feb6c81b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5884aebf5ee6599b9a6c7ea5268aa52b0536dbb9e00739bc32d8dc5feb6c81b6","first_computed_at":"2026-07-05T08:33:03.613021Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:33:03.613021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hlfebWI655ZVAhs73FGdRqPh1574pg3HzFY9q1E+eThXx+g8wSlHvk07F6H43Yi67e+5eGHHgO2iyYYli2WvDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:33:03.613450Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.11921","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb226f495df8e6477a75590baca9722872efbff7c2b7fbd48f99745261b51cc2","sha256:4d2ba9db62bb4cb975caa3c3602a5c75cb4238dd0ed66199a0d6545a51960e0c"],"state_sha256":"f997b4b5d89100b8c56e305fb226a48429ee8ce1aaf283bc3b559488191f9db9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7SZyoA2nAl+RtSDAfLEYF4+bSUs023Qu0XYEl4LMaYUHEDgxxcnMyoR/REVwxky/H8aMOPw98IEYoe7f/Q8dDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T12:04:52.389874Z","bundle_sha256":"0f6d1e1c83f9f2099c47eca388b7a4db67ab407c0cb01b089ac8f95adb809ea2"}}