{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:6EL5356ARX6QQT4VDIM53ANIGG","short_pith_number":"pith:6EL5356A","canonical_record":{"source":{"id":"2402.05128","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-05T11:58:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2139bf1b93812baf5d7daa201ec3aadff38674e0a114d1ac3b17aafdc22f1a3a","abstract_canon_sha256":"52a2e3edb16b5f546d2b9d9971aaa74a87ccf6198c3ab6a888eed04608476d49"},"schema_version":"1.0"},"canonical_sha256":"f117ddf7c08dfd084f951a19dd81a831be20a8e1e8a7911378a7bdebcc18c007","source":{"kind":"arxiv","id":"2402.05128","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.05128","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"arxiv_version","alias_value":"2402.05128v3","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05128","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"pith_short_12","alias_value":"6EL5356ARX6Q","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"pith_short_16","alias_value":"6EL5356ARX6QQT4V","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"pith_short_8","alias_value":"6EL5356A","created_at":"2026-07-05T10:03:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:6EL5356ARX6QQT4VDIM53ANIGG","target":"record","payload":{"canonical_record":{"source":{"id":"2402.05128","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-05T11:58:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2139bf1b93812baf5d7daa201ec3aadff38674e0a114d1ac3b17aafdc22f1a3a","abstract_canon_sha256":"52a2e3edb16b5f546d2b9d9971aaa74a87ccf6198c3ab6a888eed04608476d49"},"schema_version":"1.0"},"canonical_sha256":"f117ddf7c08dfd084f951a19dd81a831be20a8e1e8a7911378a7bdebcc18c007","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:41.037187Z","signature_b64":"bS9QdA7jDMhaTo5iqibGaE38TKO/N/47PP7aA99OFkiK8gpFq0xyw2dI9ol148hZLjnmZmnThqw7JL3xTMYZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f117ddf7c08dfd084f951a19dd81a831be20a8e1e8a7911378a7bdebcc18c007","last_reissued_at":"2026-07-05T10:03:41.036713Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:41.036713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.05128","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:03:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zxqkmm631TXx1YLzYnoaNBQrdP5oA0N69sS/qCX6o5vpMo0Eie6r2gGv0QHHZN269sa8ZkGd8d3WxST7uMk5DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:26:25.188598Z"},"content_sha256":"6b1a797476c6ced30a2cd6853500c5b1569517ce46035969427fe1741c0c0b0a","schema_version":"1.0","event_id":"sha256:6b1a797476c6ced30a2cd6853500c5b1569517ce46035969427fe1741c0c0b0a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:6EL5356ARX6QQT4VDIM53ANIGG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing textual textbook question answering with large language models and retrieval augmented generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ali Alkhathlan, Amani Jamal, Areej Alhothali, Hessa Abdulrahman Alawwad, Usman Naseem","submitted_at":"2024-02-05T11:58:56Z","abstract_excerpt":"Textbook question answering (TQA) is a challenging task in artificial intelligence due to the complex nature of context needed to answer complex questions. Although previous research has improved the task, there are still some limitations in textual TQA, including weak reasoning and inability to capture contextual information in the lengthy context. We propose a framework (PLRTQA) that incorporates the retrieval augmented generation (RAG) technique to handle the out-of-domain scenario where concepts are spread across different lessons, and utilize transfer learning to handle the long context a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05128","kind":"arxiv","version":3},"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/2402.05128/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:03:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R2fu4iU37gH7OKoz27BlujBgW8nyVacFAS8vj3FGLlfLklmSiNwvMOSghBjODpcrFu9AwdsGc8iQdEVh2z+WBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T22:26:25.190837Z"},"content_sha256":"3da005d3dcd5efbd9140b3dcd5929bc27b10ea848b1b259dcd421416c1a72637","schema_version":"1.0","event_id":"sha256:3da005d3dcd5efbd9140b3dcd5929bc27b10ea848b1b259dcd421416c1a72637"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6EL5356ARX6QQT4VDIM53ANIGG/bundle.json","state_url":"https://pith.science/pith/6EL5356ARX6QQT4VDIM53ANIGG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6EL5356ARX6QQT4VDIM53ANIGG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-14T22:26:25Z","links":{"resolver":"https://pith.science/pith/6EL5356ARX6QQT4VDIM53ANIGG","bundle":"https://pith.science/pith/6EL5356ARX6QQT4VDIM53ANIGG/bundle.json","state":"https://pith.science/pith/6EL5356ARX6QQT4VDIM53ANIGG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6EL5356ARX6QQT4VDIM53ANIGG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6EL5356ARX6QQT4VDIM53ANIGG","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":"52a2e3edb16b5f546d2b9d9971aaa74a87ccf6198c3ab6a888eed04608476d49","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-05T11:58:56Z","title_canon_sha256":"2139bf1b93812baf5d7daa201ec3aadff38674e0a114d1ac3b17aafdc22f1a3a"},"schema_version":"1.0","source":{"id":"2402.05128","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.05128","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"arxiv_version","alias_value":"2402.05128v3","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.05128","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"pith_short_12","alias_value":"6EL5356ARX6Q","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"pith_short_16","alias_value":"6EL5356ARX6QQT4V","created_at":"2026-07-05T10:03:41Z"},{"alias_kind":"pith_short_8","alias_value":"6EL5356A","created_at":"2026-07-05T10:03:41Z"}],"graph_snapshots":[{"event_id":"sha256:3da005d3dcd5efbd9140b3dcd5929bc27b10ea848b1b259dcd421416c1a72637","target":"graph","created_at":"2026-07-05T10:03:41Z","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/2402.05128/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Textbook question answering (TQA) is a challenging task in artificial intelligence due to the complex nature of context needed to answer complex questions. Although previous research has improved the task, there are still some limitations in textual TQA, including weak reasoning and inability to capture contextual information in the lengthy context. We propose a framework (PLRTQA) that incorporates the retrieval augmented generation (RAG) technique to handle the out-of-domain scenario where concepts are spread across different lessons, and utilize transfer learning to handle the long context a","authors_text":"Ali Alkhathlan, Amani Jamal, Areej Alhothali, Hessa Abdulrahman Alawwad, Usman Naseem","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-05T11:58:56Z","title":"Enhancing textual textbook question answering with large language models and retrieval augmented generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.05128","kind":"arxiv","version":3},"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":"sha256:6b1a797476c6ced30a2cd6853500c5b1569517ce46035969427fe1741c0c0b0a","target":"record","created_at":"2026-07-05T10:03:41Z","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":"52a2e3edb16b5f546d2b9d9971aaa74a87ccf6198c3ab6a888eed04608476d49","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-05T11:58:56Z","title_canon_sha256":"2139bf1b93812baf5d7daa201ec3aadff38674e0a114d1ac3b17aafdc22f1a3a"},"schema_version":"1.0","source":{"id":"2402.05128","kind":"arxiv","version":3}},"canonical_sha256":"f117ddf7c08dfd084f951a19dd81a831be20a8e1e8a7911378a7bdebcc18c007","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f117ddf7c08dfd084f951a19dd81a831be20a8e1e8a7911378a7bdebcc18c007","first_computed_at":"2026-07-05T10:03:41.036713Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:03:41.036713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bS9QdA7jDMhaTo5iqibGaE38TKO/N/47PP7aA99OFkiK8gpFq0xyw2dI9ol148hZLjnmZmnThqw7JL3xTMYZAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:03:41.037187Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.05128","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b1a797476c6ced30a2cd6853500c5b1569517ce46035969427fe1741c0c0b0a","sha256:3da005d3dcd5efbd9140b3dcd5929bc27b10ea848b1b259dcd421416c1a72637"],"state_sha256":"19a581f17bc0c7229b8318ec242153db2fbbc1a428cffaa0a90d7e5515d5c27f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"txJHU+8X+2/oVOHhWBx82IzB5pVUSmiQVIxXHwKSbcB8DtpTfW1KCCyd7foG6uOwKylIOnL4SbLX68Fw+dO2Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T22:26:25.198338Z","bundle_sha256":"d8279d96f60bcbb9ca3730e2dd5c8fcfedb3996cfe9556deae1bbdaa4e945dff"}}