{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:X6X7BXMAKQG43TZLHW4JBANUVM","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":"071d1df04c020225a6bf791ab161d5d2e20c878eae093b8c8034200054b5f2e9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T09:06:00Z","title_canon_sha256":"4dee65aef18054681f59a70a90fcfdabd95224f3b5a1a713b44cc4a8bb09591e"},"schema_version":"1.0","source":{"id":"2310.10196","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.10196","created_at":"2026-06-09T02:06:58Z"},{"alias_kind":"arxiv_version","alias_value":"2310.10196v3","created_at":"2026-06-09T02:06:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10196","created_at":"2026-06-09T02:06:58Z"},{"alias_kind":"pith_short_12","alias_value":"X6X7BXMAKQG4","created_at":"2026-06-09T02:06:58Z"},{"alias_kind":"pith_short_16","alias_value":"X6X7BXMAKQG43TZL","created_at":"2026-06-09T02:06:58Z"},{"alias_kind":"pith_short_8","alias_value":"X6X7BXMA","created_at":"2026-06-09T02:06:58Z"}],"graph_snapshots":[{"event_id":"sha256:fca564de35f4a4b9fdd47331af9789b47a3141a8a0aaaa0dc9954f833a4f8815","target":"graph","created_at":"2026-06-09T02:06:58Z","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/2310.10196/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record dynamic system behaviors and enable a wide range of downstream tasks. Effectively analyzing such data is crucial to unlocking their rich information content. Recent advances in large language models and other foundation models have accelerated their use in time series and spatio-temporal data mining. These approaches not only improve pattern recognition and reasoning across diverse domains but also support progress to","authors_text":"Chaoli Zhang, Haifeng Chen, Hui Xiong, James Zhang, Lei Chen, Ming Jin, Qingsong Wen, Shirui Pan, Siqiao Xue, Vincent S. Tseng, Xiaoli Li, Xue Wang, Yaxuan Kong, Yi Wang, Yuxuan Liang, Yu Zheng","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T09:06:00Z","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10196","kind":"arxiv","version":3},"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:c9ac8f8021989d0883b4e0f01b223badd6a226ff8462137a1eae7b1b516e82aa","target":"record","created_at":"2026-06-09T02:06:58Z","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":"071d1df04c020225a6bf791ab161d5d2e20c878eae093b8c8034200054b5f2e9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-16T09:06:00Z","title_canon_sha256":"4dee65aef18054681f59a70a90fcfdabd95224f3b5a1a713b44cc4a8bb09591e"},"schema_version":"1.0","source":{"id":"2310.10196","kind":"arxiv","version":3}},"canonical_sha256":"bfaff0dd80540dcdcf2b3db89081b4ab1359d6283223c579d7037d1e8b3e5f93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfaff0dd80540dcdcf2b3db89081b4ab1359d6283223c579d7037d1e8b3e5f93","first_computed_at":"2026-06-09T02:06:58.771846Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:06:58.771846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qvd1MngzjVjJaAqKxmbBNfqh/RHyhUpXics5JKb9ZDCg+RMYDyMTRyC//nerDyfipAVQB9eGZVRDgJyIDV91Ag==","signature_status":"signed_v1","signed_at":"2026-06-09T02:06:58.772766Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.10196","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9ac8f8021989d0883b4e0f01b223badd6a226ff8462137a1eae7b1b516e82aa","sha256:fca564de35f4a4b9fdd47331af9789b47a3141a8a0aaaa0dc9954f833a4f8815"],"state_sha256":"52841c346ce58373ea9bf1ce3256d4f2dca9b3c23eaeabff70cfa21e9b1e58de"}