{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BI3KOD5SSJ7OJZFSJX6PLIWCRH","short_pith_number":"pith:BI3KOD5S","canonical_record":{"source":{"id":"2403.15736","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-23T06:03:36Z","cross_cats_sorted":[],"title_canon_sha256":"12700ec05036d34a5af5ec73462386ef7b6514af4918cab7ad7eb1732b607f52","abstract_canon_sha256":"a85e15dbebd594f38f6c31e59f1a9ad0b1c137badc03ff513f31104a5d47fe3a"},"schema_version":"1.0"},"canonical_sha256":"0a36a70fb2927ee4e4b24dfcf5a2c289d7502d622af919fb9802d8f477c10f15","source":{"kind":"arxiv","id":"2403.15736","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.15736","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"arxiv_version","alias_value":"2403.15736v2","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.15736","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"pith_short_12","alias_value":"BI3KOD5SSJ7O","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"pith_short_16","alias_value":"BI3KOD5SSJ7OJZFS","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"pith_short_8","alias_value":"BI3KOD5S","created_at":"2026-07-05T09:34:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BI3KOD5SSJ7OJZFSJX6PLIWCRH","target":"record","payload":{"canonical_record":{"source":{"id":"2403.15736","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-23T06:03:36Z","cross_cats_sorted":[],"title_canon_sha256":"12700ec05036d34a5af5ec73462386ef7b6514af4918cab7ad7eb1732b607f52","abstract_canon_sha256":"a85e15dbebd594f38f6c31e59f1a9ad0b1c137badc03ff513f31104a5d47fe3a"},"schema_version":"1.0"},"canonical_sha256":"0a36a70fb2927ee4e4b24dfcf5a2c289d7502d622af919fb9802d8f477c10f15","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:34:56.169111Z","signature_b64":"wLYfI+F0uB+xBOG0WJpAySrX7mnwMF7KSmtIi5iZRy69hhvC4y47+GDcNpSUknNbxlhOWD6Y+34uby4eYE9YCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a36a70fb2927ee4e4b24dfcf5a2c289d7502d622af919fb9802d8f477c10f15","last_reissued_at":"2026-07-05T09:34:56.168650Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:34:56.168650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.15736","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:34:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XJxmWXSlf/JM343JyXcw0FGpnuO6x2pJceCWI9f3IXZoiwgpgIdk3oItnifyjZy6oOCCX9p89tbce3EM6R2kAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T03:01:27.758548Z"},"content_sha256":"e87872fd13bc86818254d5ca3c644db9fa6c9440d4326c893ac2abc1c46e74c8","schema_version":"1.0","event_id":"sha256:e87872fd13bc86818254d5ca3c644db9fa6c9440d4326c893ac2abc1c46e74c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BI3KOD5SSJ7OJZFSJX6PLIWCRH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"General LLMs as Instructors for Domain-Specific LLMs: A Sequential Fusion Method to Integrate Extraction and Editing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Huijia Liang, Qin Zhang, Tianjie Ju, Xin Zhang, Ying Fu","submitted_at":"2024-03-23T06:03:36Z","abstract_excerpt":"The substantial interest in updating Large Language Models (LLMs) without retraining from scratch is accompanied by several challenges. This is particularly true when updating LLMs with datasets that necessitate domain-expert reasoning across extensive texts, despite limited samples. We termed the scenario as the Few-Shot Domain-Expert Reasoning for Updating LLMs (FDoR-UL). Traditional methods such as Low-Rank Adaptation (LoRA) and Retrieval Augmented Generation (RAG) are inadequate for addressing this critical issue, particularly evident in our exploration of a specific medical dataset that e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.15736","kind":"arxiv","version":2},"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/2403.15736/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":"2026-07-05T09:34:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WM8h9FG9JTmi+Ag2TdB7chQqY4tK3Ax5nyo2pARUzM44+anmuGSn0vCx5UM0tcEq5B9ZaPROQ8kZ5AMwNUgJCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T03:01:27.759031Z"},"content_sha256":"dac7be3af7ac211ef2af4485db4e6a86d1c32513e9523ad4212d5dbd1265f752","schema_version":"1.0","event_id":"sha256:dac7be3af7ac211ef2af4485db4e6a86d1c32513e9523ad4212d5dbd1265f752"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/bundle.json","state_url":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/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":"2026-08-09T03:01:27Z","links":{"resolver":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH","bundle":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/bundle.json","state":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BI3KOD5SSJ7OJZFSJX6PLIWCRH","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":"a85e15dbebd594f38f6c31e59f1a9ad0b1c137badc03ff513f31104a5d47fe3a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-23T06:03:36Z","title_canon_sha256":"12700ec05036d34a5af5ec73462386ef7b6514af4918cab7ad7eb1732b607f52"},"schema_version":"1.0","source":{"id":"2403.15736","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.15736","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"arxiv_version","alias_value":"2403.15736v2","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.15736","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"pith_short_12","alias_value":"BI3KOD5SSJ7O","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"pith_short_16","alias_value":"BI3KOD5SSJ7OJZFS","created_at":"2026-07-05T09:34:56Z"},{"alias_kind":"pith_short_8","alias_value":"BI3KOD5S","created_at":"2026-07-05T09:34:56Z"}],"graph_snapshots":[{"event_id":"sha256:dac7be3af7ac211ef2af4485db4e6a86d1c32513e9523ad4212d5dbd1265f752","target":"graph","created_at":"2026-07-05T09:34:56Z","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/2403.15736/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The substantial interest in updating Large Language Models (LLMs) without retraining from scratch is accompanied by several challenges. This is particularly true when updating LLMs with datasets that necessitate domain-expert reasoning across extensive texts, despite limited samples. We termed the scenario as the Few-Shot Domain-Expert Reasoning for Updating LLMs (FDoR-UL). Traditional methods such as Low-Rank Adaptation (LoRA) and Retrieval Augmented Generation (RAG) are inadequate for addressing this critical issue, particularly evident in our exploration of a specific medical dataset that e","authors_text":"Huijia Liang, Qin Zhang, Tianjie Ju, Xin Zhang, Ying Fu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-23T06:03:36Z","title":"General LLMs as Instructors for Domain-Specific LLMs: A Sequential Fusion Method to Integrate Extraction and Editing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.15736","kind":"arxiv","version":2},"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:e87872fd13bc86818254d5ca3c644db9fa6c9440d4326c893ac2abc1c46e74c8","target":"record","created_at":"2026-07-05T09:34:56Z","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":"a85e15dbebd594f38f6c31e59f1a9ad0b1c137badc03ff513f31104a5d47fe3a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-23T06:03:36Z","title_canon_sha256":"12700ec05036d34a5af5ec73462386ef7b6514af4918cab7ad7eb1732b607f52"},"schema_version":"1.0","source":{"id":"2403.15736","kind":"arxiv","version":2}},"canonical_sha256":"0a36a70fb2927ee4e4b24dfcf5a2c289d7502d622af919fb9802d8f477c10f15","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0a36a70fb2927ee4e4b24dfcf5a2c289d7502d622af919fb9802d8f477c10f15","first_computed_at":"2026-07-05T09:34:56.168650Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:34:56.168650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wLYfI+F0uB+xBOG0WJpAySrX7mnwMF7KSmtIi5iZRy69hhvC4y47+GDcNpSUknNbxlhOWD6Y+34uby4eYE9YCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:34:56.169111Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.15736","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e87872fd13bc86818254d5ca3c644db9fa6c9440d4326c893ac2abc1c46e74c8","sha256:dac7be3af7ac211ef2af4485db4e6a86d1c32513e9523ad4212d5dbd1265f752"],"state_sha256":"34d2fcee7c8379a0a523104ace6d05224e3cee15a5ab5492b47b39d27d162d2c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+tkjLUCIgsuyIa0u9TDBqPci/XVWtXsUd32ubkYAqsGcKydkOhC7+kN8sdojh9ZLgaDaJbbpG9Kcwd5PQKsMDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T03:01:27.762263Z","bundle_sha256":"7867da57efd300133fde93d27b91d82124fec4d9c995cd50c4dfde18f431781f"}}