{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:V6S53V4XT3AB777HQJAXQB7IOY","short_pith_number":"pith:V6S53V4X","canonical_record":{"source":{"id":"2411.07763","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-12T12:52:17Z","cross_cats_sorted":["cs.AI","cs.DB"],"title_canon_sha256":"54d48cffcc7ea96dcffdebaafb866da672d94a55c59196c426519a588610724b","abstract_canon_sha256":"169dba55d204b9b2165f440ac32f8486952cab82a2d4c1e3607614e58cae46a2"},"schema_version":"1.0"},"canonical_sha256":"afa5ddd7979ec01fffe782417807e8760016ab3c579c293f043be55188bd41f5","source":{"kind":"arxiv","id":"2411.07763","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.07763","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.07763v2","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07763","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_12","alias_value":"V6S53V4XT3AB","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"V6S53V4XT3AB777H","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"V6S53V4X","created_at":"2026-07-05T10:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:V6S53V4XT3AB777HQJAXQB7IOY","target":"record","payload":{"canonical_record":{"source":{"id":"2411.07763","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-12T12:52:17Z","cross_cats_sorted":["cs.AI","cs.DB"],"title_canon_sha256":"54d48cffcc7ea96dcffdebaafb866da672d94a55c59196c426519a588610724b","abstract_canon_sha256":"169dba55d204b9b2165f440ac32f8486952cab82a2d4c1e3607614e58cae46a2"},"schema_version":"1.0"},"canonical_sha256":"afa5ddd7979ec01fffe782417807e8760016ab3c579c293f043be55188bd41f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:33.594542Z","signature_b64":"SzQ5AIeq7y1fn/OD7c3KOR4U/fHauL3smph1DjBm8IhOiDxroDEEdlRX6OOYzligoda+bYAPPJuxZbSdpv40AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afa5ddd7979ec01fffe782417807e8760016ab3c579c293f043be55188bd41f5","last_reissued_at":"2026-07-05T10:32:33.593581Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:33.593581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.07763","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":"2026-07-05T10:32:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9HT2+TNPeh6J5U/cd5uO26rswwYlUAFGU6DpU19ILlipcVCC90kBetcqQjSorCBlTInpbDCB1D+yL8/JaLezBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T18:52:05.857041Z"},"content_sha256":"3b702d621611f0b0b1df69aabbbcd856a4ef64728b763fe43799a8c45958e37a","schema_version":"1.0","event_id":"sha256:3b702d621611f0b0b1df69aabbbcd856a4ef64728b763fe43799a8c45958e37a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:V6S53V4XT3AB777HQJAXQB7IOY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.DB"],"primary_cat":"cs.CL","authors_text":"Caiming Xiong, Dongchan Shin, Fangyu Lei, Hongcheng Gao, Hongjin Su, Jixuan Chen, Pengcheng Yin, Qian Liu, Ruisheng Cao, Ruoxi Sun, Sida Wang, Tao Yu, Victor Zhong, Wenjing Hu, Yuxiao Ye, Zhaoqing Suo","submitted_at":"2024-11-12T12:52:17Z","abstract_excerpt":"Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics. We introduce Spider 2.0, an evaluation framework comprising 632 real-world text-to-SQL workflow problems derived from enterprise-level database use cases. The databases in Spider 2.0 are sourced from real data applications, often containing over 1,000 columns and stored in local or cloud database systems such as BigQuery and Snowflake. We show that solving problems in Spider"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.07763","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/2411.07763/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":"2026-07-05T10:32:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Q033GQ6LcshM8NFkysvTxKEA8kHVt5FK43YGMq8eZtC1LcmhmPQPKmMlsxQkLSOzyMU8BSo4XaJHlD5kv5UDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T18:52:05.857443Z"},"content_sha256":"9c110b2ac139c3550a0415397950d78b2482f94332828539ba4d7ba7d8145c74","schema_version":"1.0","event_id":"sha256:9c110b2ac139c3550a0415397950d78b2482f94332828539ba4d7ba7d8145c74"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V6S53V4XT3AB777HQJAXQB7IOY/bundle.json","state_url":"https://pith.science/pith/V6S53V4XT3AB777HQJAXQB7IOY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V6S53V4XT3AB777HQJAXQB7IOY/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":"2026-07-19T18:52:05Z","links":{"resolver":"https://pith.science/pith/V6S53V4XT3AB777HQJAXQB7IOY","bundle":"https://pith.science/pith/V6S53V4XT3AB777HQJAXQB7IOY/bundle.json","state":"https://pith.science/pith/V6S53V4XT3AB777HQJAXQB7IOY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V6S53V4XT3AB777HQJAXQB7IOY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:V6S53V4XT3AB777HQJAXQB7IOY","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":"169dba55d204b9b2165f440ac32f8486952cab82a2d4c1e3607614e58cae46a2","cross_cats_sorted":["cs.AI","cs.DB"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-12T12:52:17Z","title_canon_sha256":"54d48cffcc7ea96dcffdebaafb866da672d94a55c59196c426519a588610724b"},"schema_version":"1.0","source":{"id":"2411.07763","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.07763","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.07763v2","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07763","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_12","alias_value":"V6S53V4XT3AB","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"V6S53V4XT3AB777H","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"V6S53V4X","created_at":"2026-07-05T10:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:9c110b2ac139c3550a0415397950d78b2482f94332828539ba4d7ba7d8145c74","target":"graph","created_at":"2026-07-05T10:32:33Z","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/2411.07763/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics. We introduce Spider 2.0, an evaluation framework comprising 632 real-world text-to-SQL workflow problems derived from enterprise-level database use cases. The databases in Spider 2.0 are sourced from real data applications, often containing over 1,000 columns and stored in local or cloud database systems such as BigQuery and Snowflake. We show that solving problems in Spider","authors_text":"Caiming Xiong, Dongchan Shin, Fangyu Lei, Hongcheng Gao, Hongjin Su, Jixuan Chen, Pengcheng Yin, Qian Liu, Ruisheng Cao, Ruoxi Sun, Sida Wang, Tao Yu, Victor Zhong, Wenjing Hu, Yuxiao Ye, Zhaoqing Suo","cross_cats":["cs.AI","cs.DB"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-12T12:52:17Z","title":"Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.07763","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":"sha256:3b702d621611f0b0b1df69aabbbcd856a4ef64728b763fe43799a8c45958e37a","target":"record","created_at":"2026-07-05T10:32:33Z","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":"169dba55d204b9b2165f440ac32f8486952cab82a2d4c1e3607614e58cae46a2","cross_cats_sorted":["cs.AI","cs.DB"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-11-12T12:52:17Z","title_canon_sha256":"54d48cffcc7ea96dcffdebaafb866da672d94a55c59196c426519a588610724b"},"schema_version":"1.0","source":{"id":"2411.07763","kind":"arxiv","version":2}},"canonical_sha256":"afa5ddd7979ec01fffe782417807e8760016ab3c579c293f043be55188bd41f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afa5ddd7979ec01fffe782417807e8760016ab3c579c293f043be55188bd41f5","first_computed_at":"2026-07-05T10:32:33.593581Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:32:33.593581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SzQ5AIeq7y1fn/OD7c3KOR4U/fHauL3smph1DjBm8IhOiDxroDEEdlRX6OOYzligoda+bYAPPJuxZbSdpv40AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:32:33.594542Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.07763","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3b702d621611f0b0b1df69aabbbcd856a4ef64728b763fe43799a8c45958e37a","sha256:9c110b2ac139c3550a0415397950d78b2482f94332828539ba4d7ba7d8145c74"],"state_sha256":"3c547bbeb96a2390f9fb2a903dec736d485aa379d78dfe41162f480537ebc6cd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hWJ5K24wMi88SxkA/F+E2wrgEy0SHzesr7F6pmYXSGreclOdtwuRJV9t6WStz8TGRJ6vI8LS/ioD619LqYKMDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-19T18:52:05.860029Z","bundle_sha256":"f870c7033e77c8f58e75d248a243e0fb6f722cf834e0688ffb62f19a726d63e8"}}