{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:C2PUINO3V7DNPQ2X6AFUEXM5YW","short_pith_number":"pith:C2PUINO3","schema_version":"1.0","canonical_sha256":"169f4435dbafc6d7c357f00b425d9dc5899e721a27f1234173667ddb092622e2","source":{"kind":"arxiv","id":"2607.03833","version":1},"attestation_state":"computed","paper":{"title":"Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Guanhua Chen, Hanqing Wang, Jian Yang, Jiehui Zhao, Lei Yang, Yongdong chi, Yun Chen","submitted_at":"2026-07-04T11:42:56Z","abstract_excerpt":"While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues. Identifying these latent weaknesses is critical for building trustworthy database interfaces, yet current diagnostic approaches rely heavily on static, expert-defined rules, which lack the capability for systematic and automated exploration. To bridge this gap, we propose SAGE (Systematic Automated Guided Exploration), a novel framework designed to autonomously uncover latent failure patterns in LLM-based Text-to-SQL gen"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.03833","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-07-04T11:42:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2b88db6522404255d6da1b1acf385e02fccda939a927b0064ee98be08d55452c","abstract_canon_sha256":"5770d5acac98ff22a224f1644d4e60e2690265717ad16771c1ccde14a7ffc203"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:18:09.834598Z","signature_b64":"YhX2Wv5ABCDgsY+vLj0H9t7gj5fTD4L4ZrMjKcbB3apuHET3YQKgEdXnoi0Ri6mgtOGtcplXkxnwpSY1LOGgBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"169f4435dbafc6d7c357f00b425d9dc5899e721a27f1234173667ddb092622e2","last_reissued_at":"2026-07-07T02:18:09.833725Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:18:09.833725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Guanhua Chen, Hanqing Wang, Jian Yang, Jiehui Zhao, Lei Yang, Yongdong chi, Yun Chen","submitted_at":"2026-07-04T11:42:56Z","abstract_excerpt":"While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues. Identifying these latent weaknesses is critical for building trustworthy database interfaces, yet current diagnostic approaches rely heavily on static, expert-defined rules, which lack the capability for systematic and automated exploration. To bridge this gap, we propose SAGE (Systematic Automated Guided Exploration), a novel framework designed to autonomously uncover latent failure patterns in LLM-based Text-to-SQL gen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.03833","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/2607.03833/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.03833","created_at":"2026-07-07T02:18:09.833878+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.03833v1","created_at":"2026-07-07T02:18:09.833878+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.03833","created_at":"2026-07-07T02:18:09.833878+00:00"},{"alias_kind":"pith_short_12","alias_value":"C2PUINO3V7DN","created_at":"2026-07-07T02:18:09.833878+00:00"},{"alias_kind":"pith_short_16","alias_value":"C2PUINO3V7DNPQ2X","created_at":"2026-07-07T02:18:09.833878+00:00"},{"alias_kind":"pith_short_8","alias_value":"C2PUINO3","created_at":"2026-07-07T02:18:09.833878+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW","json":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW.json","graph_json":"https://pith.science/api/pith-number/C2PUINO3V7DNPQ2X6AFUEXM5YW/graph.json","events_json":"https://pith.science/api/pith-number/C2PUINO3V7DNPQ2X6AFUEXM5YW/events.json","paper":"https://pith.science/paper/C2PUINO3"},"agent_actions":{"view_html":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW","download_json":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW.json","view_paper":"https://pith.science/paper/C2PUINO3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.03833&json=true","fetch_graph":"https://pith.science/api/pith-number/C2PUINO3V7DNPQ2X6AFUEXM5YW/graph.json","fetch_events":"https://pith.science/api/pith-number/C2PUINO3V7DNPQ2X6AFUEXM5YW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW/action/storage_attestation","attest_author":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW/action/author_attestation","sign_citation":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW/action/citation_signature","submit_replication":"https://pith.science/pith/C2PUINO3V7DNPQ2X6AFUEXM5YW/action/replication_record"}},"created_at":"2026-07-07T02:18:09.833878+00:00","updated_at":"2026-07-07T02:18:09.833878+00:00"}