{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XA7AKOVZYMFWU4FNHYMVASJF3W","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ac99dea14bb20a5611142a49f8eeda9542e9a9d1e4e9e064521368edc33ce79c","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T16:50:55Z","title_canon_sha256":"ae6a5f3fca21db37443521bcff1bf6e63cf715cf159cca599d3c9d809433c219"},"schema_version":"1.0","source":{"id":"2501.02599","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.02599","created_at":"2026-07-05T09:57:09Z"},{"alias_kind":"arxiv_version","alias_value":"2501.02599v1","created_at":"2026-07-05T09:57:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.02599","created_at":"2026-07-05T09:57:09Z"},{"alias_kind":"pith_short_12","alias_value":"XA7AKOVZYMFW","created_at":"2026-07-05T09:57:09Z"},{"alias_kind":"pith_short_16","alias_value":"XA7AKOVZYMFWU4FN","created_at":"2026-07-05T09:57:09Z"},{"alias_kind":"pith_short_8","alias_value":"XA7AKOVZ","created_at":"2026-07-05T09:57:09Z"}],"graph_snapshots":[{"event_id":"sha256:e457882c82c6c6ac6ccfeb0683273fe87f694bf48788ada9b82cc7b4c07e095a","target":"graph","created_at":"2026-07-05T09:57:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.02599/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mathematical word problems (MWPs) involve the task of converting textual descriptions into mathematical equations. This poses a significant challenge in natural language processing, particularly for low-resource languages such as Bengali. This paper addresses this challenge by developing an innovative approach to solving Bengali MWPs using transformer-based models, including Basic Transformer, mT5, BanglaT5, and mBART50. To support this effort, the \"PatiGonit\" dataset was introduced, containing 10,000 Bengali math problems, and these models were fine-tuned to translate the word problems into e","authors_text":"Bidyarthi Paul, Faisal Muhammad Shah, Jalisha Jashim Era, Mirazur Rahman Zim, Tahmid Sattar Aothoi","cross_cats":["cs.AI","cs.CY","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T16:50:55Z","title":"Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.02599","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3bf1bc934cfcca0363aecac5db80978f0b6c0552cf1de2e995e748dba97a43e2","target":"record","created_at":"2026-07-05T09:57:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ac99dea14bb20a5611142a49f8eeda9542e9a9d1e4e9e064521368edc33ce79c","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-05T16:50:55Z","title_canon_sha256":"ae6a5f3fca21db37443521bcff1bf6e63cf715cf159cca599d3c9d809433c219"},"schema_version":"1.0","source":{"id":"2501.02599","kind":"arxiv","version":1}},"canonical_sha256":"b83e053ab9c30b6a70ad3e19504925dd9686d1b6afb3645da01e97f9a7cd75af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b83e053ab9c30b6a70ad3e19504925dd9686d1b6afb3645da01e97f9a7cd75af","first_computed_at":"2026-07-05T09:57:09.515815Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:57:09.515815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z47WoL0e55FMYXe5JfLFzjiAlvwRiaTlSv7yCEFwAIBL+8XlZAr+/VbUR5qsb1gt2Rmiq4Q5xC2N42mGgcFODA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:57:09.516156Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.02599","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3bf1bc934cfcca0363aecac5db80978f0b6c0552cf1de2e995e748dba97a43e2","sha256:e457882c82c6c6ac6ccfeb0683273fe87f694bf48788ada9b82cc7b4c07e095a"],"state_sha256":"6714990f97921ae712d6c25f6dbe8e03e554c23f56054d503664e5f7fd7b5b66"}