{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IHIMOXRXJVVUC7UL3VBEYTCTF5","short_pith_number":"pith:IHIMOXRX","schema_version":"1.0","canonical_sha256":"41d0c75e374d6b417e8bdd424c4c532f5cc080ee43cf26470565c7caa8608e17","source":{"kind":"arxiv","id":"2607.22624","version":1},"attestation_state":"computed","paper":{"title":"CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Minghao Yang, Yanjun Xu","submitted_at":"2026-06-17T08:51:43Z","abstract_excerpt":"Recently, there have been several works in the Text-to-SQL domain that utilize Small Language Models (SLMs) for training. These approaches achieve performance close to that of large models in generating SQL, using only the computational power of a single NVIDIA RTX 4090 GPU, while also ensuring data security. Most existing methods filter out redundant tables and columns during Schema Linking to improve Text-to-SQL accuracy. However, they do not consider the precision-recall trade-off when selecting the candidate schema subset. Our research found that both the precision and recall of Schema Lin"},"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":"2607.22624","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-17T08:51:43Z","cross_cats_sorted":[],"title_canon_sha256":"c6c181a85631dbc6137b992f6d0b2524504e868f45a2cda6471972f91fc2ee40","abstract_canon_sha256":"d72e35acc6442f1628e2a7ca7df57eb253741d0883972858cb80e80a5a0db5a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T00:21:45.680680Z","signature_b64":"nGj3Viu6+qs1bbC5ku4glZacLIvoA1R8lpXquWIQb6skrYOsmxdsMLGEBJP6e370vo+xa6+mY1ey5jj3nSXQBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41d0c75e374d6b417e8bdd424c4c532f5cc080ee43cf26470565c7caa8608e17","last_reissued_at":"2026-07-28T00:21:45.679849Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T00:21:45.679849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Minghao Yang, Yanjun Xu","submitted_at":"2026-06-17T08:51:43Z","abstract_excerpt":"Recently, there have been several works in the Text-to-SQL domain that utilize Small Language Models (SLMs) for training. These approaches achieve performance close to that of large models in generating SQL, using only the computational power of a single NVIDIA RTX 4090 GPU, while also ensuring data security. Most existing methods filter out redundant tables and columns during Schema Linking to improve Text-to-SQL accuracy. However, they do not consider the precision-recall trade-off when selecting the candidate schema subset. Our research found that both the precision and recall of Schema Lin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22624","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/2607.22624/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":"2607.22624","created_at":"2026-07-28T00:21:45.680267+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22624v1","created_at":"2026-07-28T00:21:45.680267+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22624","created_at":"2026-07-28T00:21:45.680267+00:00"},{"alias_kind":"pith_short_12","alias_value":"IHIMOXRXJVVU","created_at":"2026-07-28T00:21:45.680267+00:00"},{"alias_kind":"pith_short_16","alias_value":"IHIMOXRXJVVUC7UL","created_at":"2026-07-28T00:21:45.680267+00:00"},{"alias_kind":"pith_short_8","alias_value":"IHIMOXRX","created_at":"2026-07-28T00:21:45.680267+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/IHIMOXRXJVVUC7UL3VBEYTCTF5","json":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5.json","graph_json":"https://pith.science/api/pith-number/IHIMOXRXJVVUC7UL3VBEYTCTF5/graph.json","events_json":"https://pith.science/api/pith-number/IHIMOXRXJVVUC7UL3VBEYTCTF5/events.json","paper":"https://pith.science/paper/IHIMOXRX"},"agent_actions":{"view_html":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5","download_json":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5.json","view_paper":"https://pith.science/paper/IHIMOXRX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22624&json=true","fetch_graph":"https://pith.science/api/pith-number/IHIMOXRXJVVUC7UL3VBEYTCTF5/graph.json","fetch_events":"https://pith.science/api/pith-number/IHIMOXRXJVVUC7UL3VBEYTCTF5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5/action/storage_attestation","attest_author":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5/action/author_attestation","sign_citation":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5/action/citation_signature","submit_replication":"https://pith.science/pith/IHIMOXRXJVVUC7UL3VBEYTCTF5/action/replication_record"}},"created_at":"2026-07-28T00:21:45.680267+00:00","updated_at":"2026-07-28T00:21:45.680267+00:00"}