{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VECYECVKMM3N4QYKROAIG33XNX","short_pith_number":"pith:VECYECVK","schema_version":"1.0","canonical_sha256":"a905820aaa6336de430a8b80836f776de65428c5b7871f94c55cb9dc7b13b0ce","source":{"kind":"arxiv","id":"2506.01710","version":1},"attestation_state":"computed","paper":{"title":"Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fangyu Lei, Jinxiang Meng, Jun Zhao, Kang Liu, Shizhu He, Tinghong Chen, Yiming Huang, Yun Zhang","submitted_at":"2025-06-02T14:18:09Z","abstract_excerpt":"Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL) to table reasoning, achieving state-of-the-art performance. Through rigorous data preprocessing, rew"},"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":"2506.01710","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-02T14:18:09Z","cross_cats_sorted":[],"title_canon_sha256":"fcc064c4251008b3a716af1c5343d0db3829400a1e288c9306461d80f8e6002b","abstract_canon_sha256":"0ff795a6ec3a9d71a3ba52e107f8570f9e25871da77e42525351a8cf2a0fa1d2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:18.190933Z","signature_b64":"GwFYn3rlyC5l+YAZ7z6CMEHkDQ3uinipD2NFA/O1hXvdC+WJOVwTRpDQWlar6Xd5zYY9Y+y1okXY/nkIW94jAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a905820aaa6336de430a8b80836f776de65428c5b7871f94c55cb9dc7b13b0ce","last_reissued_at":"2026-07-05T11:14:18.190468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:18.190468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fangyu Lei, Jinxiang Meng, Jun Zhao, Kang Liu, Shizhu He, Tinghong Chen, Yiming Huang, Yun Zhang","submitted_at":"2025-06-02T14:18:09Z","abstract_excerpt":"Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL) to table reasoning, achieving state-of-the-art performance. Through rigorous data preprocessing, rew"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01710","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/2506.01710/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":"2506.01710","created_at":"2026-07-05T11:14:18.190525+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.01710v1","created_at":"2026-07-05T11:14:18.190525+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01710","created_at":"2026-07-05T11:14:18.190525+00:00"},{"alias_kind":"pith_short_12","alias_value":"VECYECVKMM3N","created_at":"2026-07-05T11:14:18.190525+00:00"},{"alias_kind":"pith_short_16","alias_value":"VECYECVKMM3N4QYK","created_at":"2026-07-05T11:14:18.190525+00:00"},{"alias_kind":"pith_short_8","alias_value":"VECYECVK","created_at":"2026-07-05T11:14:18.190525+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29905","citing_title":"StrucTab: A Structured Optimization Framework for Table Parsing","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26122","citing_title":"DocArena: Turning Raw Documents into Controllable Training Environments for Document Search Agents","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX","json":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX.json","graph_json":"https://pith.science/api/pith-number/VECYECVKMM3N4QYKROAIG33XNX/graph.json","events_json":"https://pith.science/api/pith-number/VECYECVKMM3N4QYKROAIG33XNX/events.json","paper":"https://pith.science/paper/VECYECVK"},"agent_actions":{"view_html":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX","download_json":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX.json","view_paper":"https://pith.science/paper/VECYECVK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.01710&json=true","fetch_graph":"https://pith.science/api/pith-number/VECYECVKMM3N4QYKROAIG33XNX/graph.json","fetch_events":"https://pith.science/api/pith-number/VECYECVKMM3N4QYKROAIG33XNX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX/action/storage_attestation","attest_author":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX/action/author_attestation","sign_citation":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX/action/citation_signature","submit_replication":"https://pith.science/pith/VECYECVKMM3N4QYKROAIG33XNX/action/replication_record"}},"created_at":"2026-07-05T11:14:18.190525+00:00","updated_at":"2026-07-05T11:14:18.190525+00:00"}