{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RH6HVT2BAWJ3PLMOQ4HWITQ36X","short_pith_number":"pith:RH6HVT2B","schema_version":"1.0","canonical_sha256":"89fc7acf410593b7ad8e870f644e1bf5e0c666bfd0a27131df05000bebb1bff9","source":{"kind":"arxiv","id":"2501.04425","version":1},"attestation_state":"computed","paper":{"title":"End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"H.M. Shadman Tabib, Jaber Ahmed Deedar","submitted_at":"2025-01-08T11:18:36Z","abstract_excerpt":"This work introduces systematic approach for enhancing large language models (LLMs) to address Bangla AI mathematical challenges. Through the assessment of diverse LLM configurations, fine-tuning with specific datasets, and the implementation of Retrieval-Augmented Generation (RAG), we enhanced the model's reasoning precision in a multilingual setting. Crucial discoveries indicate that customized prompting, dataset augmentation, and iterative reasoning improve the model's efficiency regarding Olympiad-level mathematical challenges."},"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":"2501.04425","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-08T11:18:36Z","cross_cats_sorted":[],"title_canon_sha256":"ebc2a2a0b0716a507a4008862d03924ff981aa77b26b444b100862d88c4e84fa","abstract_canon_sha256":"4aed7dc71fc1826a8952707ab209e99e503cc065f4c371eae5654af213be673b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:34.867682Z","signature_b64":"hNUmj5Kw3BVbEF7WXkzJZDJINVl0khhul01Lcj59vsYopV13yYheIwG+NtBh9x7uFXuwiJENosrq20+COnsFAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89fc7acf410593b7ad8e870f644e1bf5e0c666bfd0a27131df05000bebb1bff9","last_reissued_at":"2026-07-05T09:58:34.867222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:34.867222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"H.M. Shadman Tabib, Jaber Ahmed Deedar","submitted_at":"2025-01-08T11:18:36Z","abstract_excerpt":"This work introduces systematic approach for enhancing large language models (LLMs) to address Bangla AI mathematical challenges. Through the assessment of diverse LLM configurations, fine-tuning with specific datasets, and the implementation of Retrieval-Augmented Generation (RAG), we enhanced the model's reasoning precision in a multilingual setting. Crucial discoveries indicate that customized prompting, dataset augmentation, and iterative reasoning improve the model's efficiency regarding Olympiad-level mathematical challenges."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.04425","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/2501.04425/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":"2501.04425","created_at":"2026-07-05T09:58:34.867282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.04425v1","created_at":"2026-07-05T09:58:34.867282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.04425","created_at":"2026-07-05T09:58:34.867282+00:00"},{"alias_kind":"pith_short_12","alias_value":"RH6HVT2BAWJ3","created_at":"2026-07-05T09:58:34.867282+00:00"},{"alias_kind":"pith_short_16","alias_value":"RH6HVT2BAWJ3PLMO","created_at":"2026-07-05T09:58:34.867282+00:00"},{"alias_kind":"pith_short_8","alias_value":"RH6HVT2B","created_at":"2026-07-05T09:58:34.867282+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/RH6HVT2BAWJ3PLMOQ4HWITQ36X","json":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X.json","graph_json":"https://pith.science/api/pith-number/RH6HVT2BAWJ3PLMOQ4HWITQ36X/graph.json","events_json":"https://pith.science/api/pith-number/RH6HVT2BAWJ3PLMOQ4HWITQ36X/events.json","paper":"https://pith.science/paper/RH6HVT2B"},"agent_actions":{"view_html":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X","download_json":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X.json","view_paper":"https://pith.science/paper/RH6HVT2B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.04425&json=true","fetch_graph":"https://pith.science/api/pith-number/RH6HVT2BAWJ3PLMOQ4HWITQ36X/graph.json","fetch_events":"https://pith.science/api/pith-number/RH6HVT2BAWJ3PLMOQ4HWITQ36X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X/action/storage_attestation","attest_author":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X/action/author_attestation","sign_citation":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X/action/citation_signature","submit_replication":"https://pith.science/pith/RH6HVT2BAWJ3PLMOQ4HWITQ36X/action/replication_record"}},"created_at":"2026-07-05T09:58:34.867282+00:00","updated_at":"2026-07-05T09:58:34.867282+00:00"}