{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MTAO4ZFJTYSTDUQO54QJSUVIU6","short_pith_number":"pith:MTAO4ZFJ","canonical_record":{"source":{"id":"2505.19956","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-26T13:19:10Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"013f636f0c6c842907e3dd295e599985e9ec6c3f1355fd7728edb083b81fa99d","abstract_canon_sha256":"0d129684501c5931ac8732b11d8e09cb581412036a8400b956956987af79cb23"},"schema_version":"1.0"},"canonical_sha256":"64c0ee64a99e2531d20eef209952a8a79837d96c9dd14fc211a8d3a17597d8ee","source":{"kind":"arxiv","id":"2505.19956","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19956","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19956v2","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19956","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"pith_short_12","alias_value":"MTAO4ZFJTYST","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"pith_short_16","alias_value":"MTAO4ZFJTYSTDUQO","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"pith_short_8","alias_value":"MTAO4ZFJ","created_at":"2026-07-05T11:40:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MTAO4ZFJTYSTDUQO54QJSUVIU6","target":"record","payload":{"canonical_record":{"source":{"id":"2505.19956","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-26T13:19:10Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"013f636f0c6c842907e3dd295e599985e9ec6c3f1355fd7728edb083b81fa99d","abstract_canon_sha256":"0d129684501c5931ac8732b11d8e09cb581412036a8400b956956987af79cb23"},"schema_version":"1.0"},"canonical_sha256":"64c0ee64a99e2531d20eef209952a8a79837d96c9dd14fc211a8d3a17597d8ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:55.114449Z","signature_b64":"hxpExi8W1ctGDg94iXlSAcrQZyFUeMFkz/ZySoJSGrmVlXUntzktBCtwWnDs3CerjFTjQhZdEmoMc4NHUi3tBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64c0ee64a99e2531d20eef209952a8a79837d96c9dd14fc211a8d3a17597d8ee","last_reissued_at":"2026-07-05T11:40:55.113950Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:55.113950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.19956","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":"2026-07-05T11:40:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JiUiRKd6/qB8Awh9eYcQM3eLydihnUQLUzPqed4ftAPD2qos2hZY/ehmvmgU6fVeaA6qf3QPUIIz77YJIuK3DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:51:05.627376Z"},"content_sha256":"b344ac0c3c8d8c56dfb163c52a0c50379179e0f0dc80e54aa0be5f8791a2763c","schema_version":"1.0","event_id":"sha256:b344ac0c3c8d8c56dfb163c52a0c50379179e0f0dc80e54aa0be5f8791a2763c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MTAO4ZFJTYSTDUQO54QJSUVIU6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Jaehoon Lee, Jee-Hyong Lee, Jihyung Lee, Jin-Seop Lee, YunSeok Choi","submitted_at":"2025-05-26T13:19:10Z","abstract_excerpt":"Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smaller LLMs (e.g., Llama 3.1-8B) are used. This indicates that these methods heavily rely on the intrinsic capabilities of hyper-scaled LLMs, rather than effectively retrieving useful demonstrations. In this paper, we propose a novel approach for effectively retrieving demonstrations and generating SQL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19956","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/2505.19956/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":"2026-07-05T11:40:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nl89zXH4poaLdasDt9PxUzOP7QswjIP/fjnnHY0e2LnGknJL1Yq5N2rPVxfN88V4r6aiASNIEANnTviH1OR+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:51:05.627880Z"},"content_sha256":"cdba082b238b30e41966a5aaf7f321288e661689a8b3f16918ff8bc030c88154","schema_version":"1.0","event_id":"sha256:cdba082b238b30e41966a5aaf7f321288e661689a8b3f16918ff8bc030c88154"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MTAO4ZFJTYSTDUQO54QJSUVIU6/bundle.json","state_url":"https://pith.science/pith/MTAO4ZFJTYSTDUQO54QJSUVIU6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MTAO4ZFJTYSTDUQO54QJSUVIU6/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":"2026-08-08T11:51:05Z","links":{"resolver":"https://pith.science/pith/MTAO4ZFJTYSTDUQO54QJSUVIU6","bundle":"https://pith.science/pith/MTAO4ZFJTYSTDUQO54QJSUVIU6/bundle.json","state":"https://pith.science/pith/MTAO4ZFJTYSTDUQO54QJSUVIU6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MTAO4ZFJTYSTDUQO54QJSUVIU6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MTAO4ZFJTYSTDUQO54QJSUVIU6","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":"0d129684501c5931ac8732b11d8e09cb581412036a8400b956956987af79cb23","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-26T13:19:10Z","title_canon_sha256":"013f636f0c6c842907e3dd295e599985e9ec6c3f1355fd7728edb083b81fa99d"},"schema_version":"1.0","source":{"id":"2505.19956","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19956","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19956v2","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19956","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"pith_short_12","alias_value":"MTAO4ZFJTYST","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"pith_short_16","alias_value":"MTAO4ZFJTYSTDUQO","created_at":"2026-07-05T11:40:55Z"},{"alias_kind":"pith_short_8","alias_value":"MTAO4ZFJ","created_at":"2026-07-05T11:40:55Z"}],"graph_snapshots":[{"event_id":"sha256:cdba082b238b30e41966a5aaf7f321288e661689a8b3f16918ff8bc030c88154","target":"graph","created_at":"2026-07-05T11:40:55Z","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/2505.19956/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smaller LLMs (e.g., Llama 3.1-8B) are used. This indicates that these methods heavily rely on the intrinsic capabilities of hyper-scaled LLMs, rather than effectively retrieving useful demonstrations. In this paper, we propose a novel approach for effectively retrieving demonstrations and generating SQL","authors_text":"Jaehoon Lee, Jee-Hyong Lee, Jihyung Lee, Jin-Seop Lee, YunSeok Choi","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-26T13:19:10Z","title":"DCG-SQL: Enhancing In-Context Learning for Text-to-SQL with Deep Contextual Schema Link Graph"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19956","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:b344ac0c3c8d8c56dfb163c52a0c50379179e0f0dc80e54aa0be5f8791a2763c","target":"record","created_at":"2026-07-05T11:40:55Z","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":"0d129684501c5931ac8732b11d8e09cb581412036a8400b956956987af79cb23","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-26T13:19:10Z","title_canon_sha256":"013f636f0c6c842907e3dd295e599985e9ec6c3f1355fd7728edb083b81fa99d"},"schema_version":"1.0","source":{"id":"2505.19956","kind":"arxiv","version":2}},"canonical_sha256":"64c0ee64a99e2531d20eef209952a8a79837d96c9dd14fc211a8d3a17597d8ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64c0ee64a99e2531d20eef209952a8a79837d96c9dd14fc211a8d3a17597d8ee","first_computed_at":"2026-07-05T11:40:55.113950Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:40:55.113950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hxpExi8W1ctGDg94iXlSAcrQZyFUeMFkz/ZySoJSGrmVlXUntzktBCtwWnDs3CerjFTjQhZdEmoMc4NHUi3tBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:40:55.114449Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.19956","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b344ac0c3c8d8c56dfb163c52a0c50379179e0f0dc80e54aa0be5f8791a2763c","sha256:cdba082b238b30e41966a5aaf7f321288e661689a8b3f16918ff8bc030c88154"],"state_sha256":"c5b6f3ce30c6dbaa09e31868e52f798381f5d5f5ece7b43854bd4f7e1484f90d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m9DH1fn0Rj7SUdYj1IIN3QCdI6E690shDm35G7FmBAS3tGXvvHkSKaRipef5QGKM8wmQWebzS+Z8ZpQ8ToPvAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T11:51:05.633070Z","bundle_sha256":"e65a43ea76e20ebd1318719a6aabaf58dcd86c292973d68a30ace21d43d0c512"}}