{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:OR44674QYRNVIK57XE6KKCDBYZ","short_pith_number":"pith:OR44674Q","canonical_record":{"source":{"id":"2403.18365","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-27T08:57:21Z","cross_cats_sorted":[],"title_canon_sha256":"cc258d071841a6a3477c6d962b0292ec891e45bf529041214a907ca0a01a0764","abstract_canon_sha256":"d76aa329c52d07da0f96fde44e5de5ada703136a86a01cca26ea13892897579c"},"schema_version":"1.0"},"canonical_sha256":"7479cf7f90c45b542bbfb93ca50861c6713909c4c51ceaadc78cc78186b6decb","source":{"kind":"arxiv","id":"2403.18365","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.18365","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"arxiv_version","alias_value":"2403.18365v1","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18365","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_12","alias_value":"OR44674QYRNV","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_16","alias_value":"OR44674QYRNVIK57","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_8","alias_value":"OR44674Q","created_at":"2026-07-05T08:01:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:OR44674QYRNVIK57XE6KKCDBYZ","target":"record","payload":{"canonical_record":{"source":{"id":"2403.18365","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-27T08:57:21Z","cross_cats_sorted":[],"title_canon_sha256":"cc258d071841a6a3477c6d962b0292ec891e45bf529041214a907ca0a01a0764","abstract_canon_sha256":"d76aa329c52d07da0f96fde44e5de5ada703136a86a01cca26ea13892897579c"},"schema_version":"1.0"},"canonical_sha256":"7479cf7f90c45b542bbfb93ca50861c6713909c4c51ceaadc78cc78186b6decb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:21.750144Z","signature_b64":"zSjpVBBMT16+Aa4uUjQ9qlMFvel5SCBY4G/lEPjmYzZOPM0TL/1DQqFzPvZnBP3rEFP9Yq5ti1NRkeehK8WYDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7479cf7f90c45b542bbfb93ca50861c6713909c4c51ceaadc78cc78186b6decb","last_reissued_at":"2026-07-05T08:01:21.749726Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:21.749726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.18365","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:01:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"57uG2oVXMiQUm1lD/3PnZwYU/WknwQW9jEFamPaTbZqKp/IFgQ0DR+Io/Pp0VoQSSgY7hItGsfSl50BdGyIfBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:58:40.810678Z"},"content_sha256":"b445532b893d6e66e69d54cdf003330b1f16fa20a913bba474a6b5ca8dab29e7","schema_version":"1.0","event_id":"sha256:b445532b893d6e66e69d54cdf003330b1f16fa20a913bba474a6b5ca8dab29e7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:OR44674QYRNVIK57XE6KKCDBYZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chong Chen, Haitao Li, Jia Chen, Qian Dong, Qingyao Ai, Qi Tian, Yiqun Liu, Zhijing Wu","submitted_at":"2024-03-27T08:57:21Z","abstract_excerpt":"Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc. To address this issue, previous approaches either conduct continuous pre-training with domain-specific data or employ retrieval augmentation to support general LLMs. Unfortunately, these strategies are either cost-intensive or unreliable in practical applications. To this end, we present a novel framework na"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18365","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/2403.18365/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":"2026-07-05T08:01:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+q8VJ7YwwSiPPf6WltFXAYdMvZ+RuCAJCvFnEUc32DZ5UZq09J2NV0hbmEuXcE+Pa8LYoXArKss6QxPJjiHzCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T08:58:40.811194Z"},"content_sha256":"b6334812b77756575e5ac0405378f4e834c4694e203e04cf2055e115c81878ba","schema_version":"1.0","event_id":"sha256:b6334812b77756575e5ac0405378f4e834c4694e203e04cf2055e115c81878ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OR44674QYRNVIK57XE6KKCDBYZ/bundle.json","state_url":"https://pith.science/pith/OR44674QYRNVIK57XE6KKCDBYZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OR44674QYRNVIK57XE6KKCDBYZ/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":"2026-08-06T08:58:40Z","links":{"resolver":"https://pith.science/pith/OR44674QYRNVIK57XE6KKCDBYZ","bundle":"https://pith.science/pith/OR44674QYRNVIK57XE6KKCDBYZ/bundle.json","state":"https://pith.science/pith/OR44674QYRNVIK57XE6KKCDBYZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OR44674QYRNVIK57XE6KKCDBYZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OR44674QYRNVIK57XE6KKCDBYZ","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":"d76aa329c52d07da0f96fde44e5de5ada703136a86a01cca26ea13892897579c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-27T08:57:21Z","title_canon_sha256":"cc258d071841a6a3477c6d962b0292ec891e45bf529041214a907ca0a01a0764"},"schema_version":"1.0","source":{"id":"2403.18365","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.18365","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"arxiv_version","alias_value":"2403.18365v1","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18365","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_12","alias_value":"OR44674QYRNV","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_16","alias_value":"OR44674QYRNVIK57","created_at":"2026-07-05T08:01:21Z"},{"alias_kind":"pith_short_8","alias_value":"OR44674Q","created_at":"2026-07-05T08:01:21Z"}],"graph_snapshots":[{"event_id":"sha256:b6334812b77756575e5ac0405378f4e834c4694e203e04cf2055e115c81878ba","target":"graph","created_at":"2026-07-05T08:01:21Z","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/2403.18365/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc. To address this issue, previous approaches either conduct continuous pre-training with domain-specific data or employ retrieval augmentation to support general LLMs. Unfortunately, these strategies are either cost-intensive or unreliable in practical applications. To this end, we present a novel framework na","authors_text":"Chong Chen, Haitao Li, Jia Chen, Qian Dong, Qingyao Ai, Qi Tian, Yiqun Liu, Zhijing Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-27T08:57:21Z","title":"BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18365","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:b445532b893d6e66e69d54cdf003330b1f16fa20a913bba474a6b5ca8dab29e7","target":"record","created_at":"2026-07-05T08:01:21Z","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":"d76aa329c52d07da0f96fde44e5de5ada703136a86a01cca26ea13892897579c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-27T08:57:21Z","title_canon_sha256":"cc258d071841a6a3477c6d962b0292ec891e45bf529041214a907ca0a01a0764"},"schema_version":"1.0","source":{"id":"2403.18365","kind":"arxiv","version":1}},"canonical_sha256":"7479cf7f90c45b542bbfb93ca50861c6713909c4c51ceaadc78cc78186b6decb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7479cf7f90c45b542bbfb93ca50861c6713909c4c51ceaadc78cc78186b6decb","first_computed_at":"2026-07-05T08:01:21.749726Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:01:21.749726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zSjpVBBMT16+Aa4uUjQ9qlMFvel5SCBY4G/lEPjmYzZOPM0TL/1DQqFzPvZnBP3rEFP9Yq5ti1NRkeehK8WYDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:01:21.750144Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.18365","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b445532b893d6e66e69d54cdf003330b1f16fa20a913bba474a6b5ca8dab29e7","sha256:b6334812b77756575e5ac0405378f4e834c4694e203e04cf2055e115c81878ba"],"state_sha256":"3353ff771a06c019c7e0264fa2796dfa816b597b19d82026df5c073ded5649e0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"txfX9nV5H0QyZbFkTFnmavq+5z1ENH3yH7hbVmhUArgBPsDkt7rKXT55ZGcBy3GJQEj7HhJru7kVOCg8NQNpBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T08:58:40.816180Z","bundle_sha256":"6b592aeb42ac1f98a06716e54bc3dd8d4a97a38b8d2d32ab8404211f34451d4a"}}