{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FZUTI6O3WYUT4DMVFNTLUXNKZW","short_pith_number":"pith:FZUTI6O3","schema_version":"1.0","canonical_sha256":"2e693479dbb6293e0d952b66ba5daacdac9178af196188a65737002e2e3b03c1","source":{"kind":"arxiv","id":"2407.05952","version":3},"attestation_state":"computed","paper":{"title":"H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.DB","authors_text":"Chandan K. Reddy, Dan Roth, Nikhil Abhyankar, Vivek Gupta","submitted_at":"2024-06-29T21:24:19Z","abstract_excerpt":"Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data analysis. Existing methods employ either textual reasoning, which excels in semantic interpretation but struggles with mathematical operations, or symbolic reasoning, which handles computations well but lacks semantic understanding. This paper introduces a novel algorithm H-STAR that integrates both symbolic and semantic (textual) approaches in a two-stage process to address these limitations. H-STAR employs: (1) step-wi"},"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":"2407.05952","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.DB","submitted_at":"2024-06-29T21:24:19Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG"],"title_canon_sha256":"2ea8c04b7908deb356943324385fba232d7340c3462fc743de0c28364ecfaffb","abstract_canon_sha256":"180cea37037396d755721f3d60e6edb9f37c0b1dffb3a9628c7d90b2419ba276"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:45:09.934199Z","signature_b64":"CQkbkb039Bi8m/xNNF3HczJ5f2W2D0Xg6ZhFbSfRb0QeG8hkwbCUjhJdM6pqlnOfEaHt5lHSAGztzsXkR/xnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e693479dbb6293e0d952b66ba5daacdac9178af196188a65737002e2e3b03c1","last_reissued_at":"2026-07-05T10:45:09.933693Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:45:09.933693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG"],"primary_cat":"cs.DB","authors_text":"Chandan K. Reddy, Dan Roth, Nikhil Abhyankar, Vivek Gupta","submitted_at":"2024-06-29T21:24:19Z","abstract_excerpt":"Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data analysis. Existing methods employ either textual reasoning, which excels in semantic interpretation but struggles with mathematical operations, or symbolic reasoning, which handles computations well but lacks semantic understanding. This paper introduces a novel algorithm H-STAR that integrates both symbolic and semantic (textual) approaches in a two-stage process to address these limitations. H-STAR employs: (1) step-wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.05952","kind":"arxiv","version":3},"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/2407.05952/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":"2407.05952","created_at":"2026-07-05T10:45:09.933752+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.05952v3","created_at":"2026-07-05T10:45:09.933752+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.05952","created_at":"2026-07-05T10:45:09.933752+00:00"},{"alias_kind":"pith_short_12","alias_value":"FZUTI6O3WYUT","created_at":"2026-07-05T10:45:09.933752+00:00"},{"alias_kind":"pith_short_16","alias_value":"FZUTI6O3WYUT4DMV","created_at":"2026-07-05T10:45:09.933752+00:00"},{"alias_kind":"pith_short_8","alias_value":"FZUTI6O3","created_at":"2026-07-05T10:45:09.933752+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/FZUTI6O3WYUT4DMVFNTLUXNKZW","json":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW.json","graph_json":"https://pith.science/api/pith-number/FZUTI6O3WYUT4DMVFNTLUXNKZW/graph.json","events_json":"https://pith.science/api/pith-number/FZUTI6O3WYUT4DMVFNTLUXNKZW/events.json","paper":"https://pith.science/paper/FZUTI6O3"},"agent_actions":{"view_html":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW","download_json":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW.json","view_paper":"https://pith.science/paper/FZUTI6O3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.05952&json=true","fetch_graph":"https://pith.science/api/pith-number/FZUTI6O3WYUT4DMVFNTLUXNKZW/graph.json","fetch_events":"https://pith.science/api/pith-number/FZUTI6O3WYUT4DMVFNTLUXNKZW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW/action/storage_attestation","attest_author":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW/action/author_attestation","sign_citation":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW/action/citation_signature","submit_replication":"https://pith.science/pith/FZUTI6O3WYUT4DMVFNTLUXNKZW/action/replication_record"}},"created_at":"2026-07-05T10:45:09.933752+00:00","updated_at":"2026-07-05T10:45:09.933752+00:00"}