{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QGGUBFGKQBIA5VQUG2EI6TGAQB","short_pith_number":"pith:QGGUBFGK","schema_version":"1.0","canonical_sha256":"818d4094ca80500ed61436888f4cc080753ac764de0b60952ed86a3eb4a0dfde","source":{"kind":"arxiv","id":"2311.12289","version":1},"attestation_state":"computed","paper":{"title":"ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Anurag Acharya, Sai Munikoti, Sameera Horawalavithana, Sridevi Wagle","submitted_at":"2023-11-21T02:02:46Z","abstract_excerpt":"Large language models record impressive performance on many natural language processing tasks. However, their knowledge capacity is limited to the pretraining corpus. Retrieval augmentation offers an effective solution by retrieving context from external knowledge sources to complement the language model. However, existing retrieval augmentation techniques ignore the structural relationships between these documents. Furthermore, retrieval models are not explored much in scientific tasks, especially in regard to the faithfulness of retrieved documents. In this paper, we propose a novel structur"},"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":"2311.12289","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-21T02:02:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7ab5342b0e298afbd689462c1b27b9b056f15b734e10632e783626e4e3df28a2","abstract_canon_sha256":"e72ba1093d5463cfe34a7acad19652e3d490d4eeff6bed62d9b625e40227180d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:15:03.089054Z","signature_b64":"koQlJ60GbalfcSJHClvPtu1WWCXyfZdujSoj6T+JAbbgonttVV2sQjJYAHmG7O28ANJILftUH9Oha/Kt6lzPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"818d4094ca80500ed61436888f4cc080753ac764de0b60952ed86a3eb4a0dfde","last_reissued_at":"2026-07-05T07:15:03.088568Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:15:03.088568Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Anurag Acharya, Sai Munikoti, Sameera Horawalavithana, Sridevi Wagle","submitted_at":"2023-11-21T02:02:46Z","abstract_excerpt":"Large language models record impressive performance on many natural language processing tasks. However, their knowledge capacity is limited to the pretraining corpus. Retrieval augmentation offers an effective solution by retrieving context from external knowledge sources to complement the language model. However, existing retrieval augmentation techniques ignore the structural relationships between these documents. Furthermore, retrieval models are not explored much in scientific tasks, especially in regard to the faithfulness of retrieved documents. In this paper, we propose a novel structur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12289","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/2311.12289/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":"2311.12289","created_at":"2026-07-05T07:15:03.088627+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.12289v1","created_at":"2026-07-05T07:15:03.088627+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12289","created_at":"2026-07-05T07:15:03.088627+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGGUBFGKQBIA","created_at":"2026-07-05T07:15:03.088627+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGGUBFGKQBIA5VQU","created_at":"2026-07-05T07:15:03.088627+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGGUBFGK","created_at":"2026-07-05T07:15:03.088627+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18765","citing_title":"STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2501.00309","citing_title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","ref_index":300,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB","json":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB.json","graph_json":"https://pith.science/api/pith-number/QGGUBFGKQBIA5VQUG2EI6TGAQB/graph.json","events_json":"https://pith.science/api/pith-number/QGGUBFGKQBIA5VQUG2EI6TGAQB/events.json","paper":"https://pith.science/paper/QGGUBFGK"},"agent_actions":{"view_html":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB","download_json":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB.json","view_paper":"https://pith.science/paper/QGGUBFGK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.12289&json=true","fetch_graph":"https://pith.science/api/pith-number/QGGUBFGKQBIA5VQUG2EI6TGAQB/graph.json","fetch_events":"https://pith.science/api/pith-number/QGGUBFGKQBIA5VQUG2EI6TGAQB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB/action/storage_attestation","attest_author":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB/action/author_attestation","sign_citation":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB/action/citation_signature","submit_replication":"https://pith.science/pith/QGGUBFGKQBIA5VQUG2EI6TGAQB/action/replication_record"}},"created_at":"2026-07-05T07:15:03.088627+00:00","updated_at":"2026-07-05T07:15:03.088627+00:00"}