{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:W3JMU33QDBVQY6YQCTHHUY33UX","short_pith_number":"pith:W3JMU33Q","schema_version":"1.0","canonical_sha256":"b6d2ca6f70186b0c7b1014ce7a637ba5fef4665559d57a2fbf4bebaa8a3f1b6d","source":{"kind":"arxiv","id":"2205.10714","version":2},"attestation_state":"computed","paper":{"title":"Interpretable Proof Generation via Iterative Backward Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hanhao Qu, Jun Gao, Liang Ding, Ruifeng Xu, Yu Cao","submitted_at":"2022-05-22T02:44:14Z","abstract_excerpt":"We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual rules and facts to find out the related proof path and derive the final answer. We handle the limitations of existed works in two folds: 1) enhance the interpretability of reasoning procedures with detailed tracking, by predicting nodes and edges in the proof path iteratively backward from the question; 2) promote the efficiency and accuracy via reasoning on the elaborate representations of nodes and histor"},"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":"2205.10714","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-22T02:44:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e7faf7442b6273fb8927000f40e70d59469a66e90118c88d0d0938a4145b362e","abstract_canon_sha256":"ed83e53d802774ec6cf5813bb1ea52d39e2234c5c942676fb1fb87979e52cc73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:26:04.703869Z","signature_b64":"ot96Usj/Z5E6+IM8HbPv2Wk6p+GAp2kneQSvb/WU9jBV+bR4soCqbL/ctsRmc3GVHWxqEXAsGjc+TbghV8BDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6d2ca6f70186b0c7b1014ce7a637ba5fef4665559d57a2fbf4bebaa8a3f1b6d","last_reissued_at":"2026-07-05T04:26:04.703382Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:26:04.703382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpretable Proof Generation via Iterative Backward Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hanhao Qu, Jun Gao, Liang Ding, Ruifeng Xu, Yu Cao","submitted_at":"2022-05-22T02:44:14Z","abstract_excerpt":"We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual rules and facts to find out the related proof path and derive the final answer. We handle the limitations of existed works in two folds: 1) enhance the interpretability of reasoning procedures with detailed tracking, by predicting nodes and edges in the proof path iteratively backward from the question; 2) promote the efficiency and accuracy via reasoning on the elaborate representations of nodes and histor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.10714","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/2205.10714/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":"2205.10714","created_at":"2026-07-05T04:26:04.703439+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.10714v2","created_at":"2026-07-05T04:26:04.703439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.10714","created_at":"2026-07-05T04:26:04.703439+00:00"},{"alias_kind":"pith_short_12","alias_value":"W3JMU33QDBVQ","created_at":"2026-07-05T04:26:04.703439+00:00"},{"alias_kind":"pith_short_16","alias_value":"W3JMU33QDBVQY6YQ","created_at":"2026-07-05T04:26:04.703439+00:00"},{"alias_kind":"pith_short_8","alias_value":"W3JMU33Q","created_at":"2026-07-05T04:26:04.703439+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/W3JMU33QDBVQY6YQCTHHUY33UX","json":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX.json","graph_json":"https://pith.science/api/pith-number/W3JMU33QDBVQY6YQCTHHUY33UX/graph.json","events_json":"https://pith.science/api/pith-number/W3JMU33QDBVQY6YQCTHHUY33UX/events.json","paper":"https://pith.science/paper/W3JMU33Q"},"agent_actions":{"view_html":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX","download_json":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX.json","view_paper":"https://pith.science/paper/W3JMU33Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.10714&json=true","fetch_graph":"https://pith.science/api/pith-number/W3JMU33QDBVQY6YQCTHHUY33UX/graph.json","fetch_events":"https://pith.science/api/pith-number/W3JMU33QDBVQY6YQCTHHUY33UX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX/action/storage_attestation","attest_author":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX/action/author_attestation","sign_citation":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX/action/citation_signature","submit_replication":"https://pith.science/pith/W3JMU33QDBVQY6YQCTHHUY33UX/action/replication_record"}},"created_at":"2026-07-05T04:26:04.703439+00:00","updated_at":"2026-07-05T04:26:04.703439+00:00"}