{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KZYF6UUHQKKAVMLAHEXZSYWH3I","short_pith_number":"pith:KZYF6UUH","schema_version":"1.0","canonical_sha256":"56705f528782940ab160392f9962c7da1e33e5174303c7d6cfa500b73752f26d","source":{"kind":"arxiv","id":"2205.15683","version":1},"attestation_state":"computed","paper":{"title":"Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Deepak P, Marco Fisichella, Sairam Gurajada, Savitha Sam Abraham, Sowmya S Sundaram","submitted_at":"2022-05-31T10:51:25Z","abstract_excerpt":"From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we are still miles away from building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challe"},"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":"2205.15683","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-05-31T10:51:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"995afee133f486a3d0a6105d061932b8db0d49e4520d263493459e43f4026ae1","abstract_canon_sha256":"53c0100ac2c55fe5ffbd44be385d916d8a5d8d8430cc1f5055cc4b7bb9770571"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:28:01.486046Z","signature_b64":"JjbMTlZIKzREPxkesvvpv/3glFpRpCdMi4zB/rEqDhJKhPuQ/p/WCCXtT/PtXijKO5sunab4CBRBkkBuP1BUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"56705f528782940ab160392f9962c7da1e33e5174303c7d6cfa500b73752f26d","last_reissued_at":"2026-07-05T04:28:01.485528Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:28:01.485528Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Why are NLP Models Fumbling at Elementary Math? A Survey of Deep Learning based Word Problem Solvers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Deepak P, Marco Fisichella, Sairam Gurajada, Savitha Sam Abraham, Sowmya S Sundaram","submitted_at":"2022-05-31T10:51:25Z","abstract_excerpt":"From the latter half of the last decade, there has been a growing interest in developing algorithms for automatically solving mathematical word problems (MWP). It is a challenging and unique task that demands blending surface level text pattern recognition with mathematical reasoning. In spite of extensive research, we are still miles away from building robust representations of elementary math word problems and effective solutions for the general task. In this paper, we critically examine the various models that have been developed for solving word problems, their pros and cons and the challe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15683","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/2205.15683/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":"2205.15683","created_at":"2026-07-05T04:28:01.485595+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.15683v1","created_at":"2026-07-05T04:28:01.485595+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15683","created_at":"2026-07-05T04:28:01.485595+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZYF6UUHQKKA","created_at":"2026-07-05T04:28:01.485595+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZYF6UUHQKKAVMLA","created_at":"2026-07-05T04:28:01.485595+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZYF6UUH","created_at":"2026-07-05T04:28:01.485595+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08728","citing_title":"Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I","json":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I.json","graph_json":"https://pith.science/api/pith-number/KZYF6UUHQKKAVMLAHEXZSYWH3I/graph.json","events_json":"https://pith.science/api/pith-number/KZYF6UUHQKKAVMLAHEXZSYWH3I/events.json","paper":"https://pith.science/paper/KZYF6UUH"},"agent_actions":{"view_html":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I","download_json":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I.json","view_paper":"https://pith.science/paper/KZYF6UUH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.15683&json=true","fetch_graph":"https://pith.science/api/pith-number/KZYF6UUHQKKAVMLAHEXZSYWH3I/graph.json","fetch_events":"https://pith.science/api/pith-number/KZYF6UUHQKKAVMLAHEXZSYWH3I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I/action/storage_attestation","attest_author":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I/action/author_attestation","sign_citation":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I/action/citation_signature","submit_replication":"https://pith.science/pith/KZYF6UUHQKKAVMLAHEXZSYWH3I/action/replication_record"}},"created_at":"2026-07-05T04:28:01.485595+00:00","updated_at":"2026-07-05T04:28:01.485595+00:00"}