{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:I2MFC5PNFUMZMTZ5H6XYQMLEUP","short_pith_number":"pith:I2MFC5PN","canonical_record":{"source":{"id":"2403.06414","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-11T03:55:24Z","cross_cats_sorted":[],"title_canon_sha256":"f1eaab1051e013500ccece3b52f3726655d5bb397656cf7ef4a8152f3ff2e6ad","abstract_canon_sha256":"ffefa970d0c5d00f6ed620dde45807a13770a9fc726d6ff523e183ef3037b6e8"},"schema_version":"1.0"},"canonical_sha256":"46985175ed2d19964f3d3faf883164a3e3f24a8d9bc4c49799b4b3be3bbc3868","source":{"kind":"arxiv","id":"2403.06414","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.06414","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"arxiv_version","alias_value":"2403.06414v1","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06414","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"pith_short_12","alias_value":"I2MFC5PNFUMZ","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"pith_short_16","alias_value":"I2MFC5PNFUMZMTZ5","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"pith_short_8","alias_value":"I2MFC5PN","created_at":"2026-07-05T07:54:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:I2MFC5PNFUMZMTZ5H6XYQMLEUP","target":"record","payload":{"canonical_record":{"source":{"id":"2403.06414","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-11T03:55:24Z","cross_cats_sorted":[],"title_canon_sha256":"f1eaab1051e013500ccece3b52f3726655d5bb397656cf7ef4a8152f3ff2e6ad","abstract_canon_sha256":"ffefa970d0c5d00f6ed620dde45807a13770a9fc726d6ff523e183ef3037b6e8"},"schema_version":"1.0"},"canonical_sha256":"46985175ed2d19964f3d3faf883164a3e3f24a8d9bc4c49799b4b3be3bbc3868","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:37.766143Z","signature_b64":"wl92qzoquJ7Ph2OFt9mzMJibx+x7tqMlAZ5wNWOk5InVNkpD2oUWVyn3/fDsl+nYYxNL3JfOq0vlQA/E6NbWCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46985175ed2d19964f3d3faf883164a3e3f24a8d9bc4c49799b4b3be3bbc3868","last_reissued_at":"2026-07-05T07:54:37.765683Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:37.765683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.06414","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":"2026-07-05T07:54:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"io6Iodf4PqvTcBVvAIyRhnHuBhRphJt/KuXzxTEpCTdO77dD8rhM/7FLQv8xfc8umQuT0DNF3j8uQ3eqdOIXCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:03:04.772624Z"},"content_sha256":"715505d20e72d8b61bf5cfc0a8d38cf1b2f3c3541e30809ba4315c74dd1bf599","schema_version":"1.0","event_id":"sha256:715505d20e72d8b61bf5cfc0a8d38cf1b2f3c3541e30809ba4315c74dd1bf599"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:I2MFC5PNFUMZMTZ5H6XYQMLEUP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evolving Knowledge Distillation with Large Language Models and Active Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changlong Sun, Chengyuan Liu, Fei Wu, Fubang Zhao, Kun Kuang, Yangyang Kang, Zhuoren Jiang","submitted_at":"2024-03-11T03:55:24Z","abstract_excerpt":"Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. Nonetheless, these works have mainly focused on the direct use of LLMs for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06414","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/2403.06414/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":"2026-07-05T07:54:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dzry5MMdn4O4SiM4vfAxtEnocLiEiTGdoiWix5bMDw8Vx1gIyv1iLtdYhQV+1mcpym7b2XMXa+BoSGk43l7iDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:03:04.773131Z"},"content_sha256":"15668cc6fe4f2c15a016543f1ba1aeeb5308f76becc89308b591898e941a7497","schema_version":"1.0","event_id":"sha256:15668cc6fe4f2c15a016543f1ba1aeeb5308f76becc89308b591898e941a7497"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I2MFC5PNFUMZMTZ5H6XYQMLEUP/bundle.json","state_url":"https://pith.science/pith/I2MFC5PNFUMZMTZ5H6XYQMLEUP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I2MFC5PNFUMZMTZ5H6XYQMLEUP/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":"2026-08-08T18:03:04Z","links":{"resolver":"https://pith.science/pith/I2MFC5PNFUMZMTZ5H6XYQMLEUP","bundle":"https://pith.science/pith/I2MFC5PNFUMZMTZ5H6XYQMLEUP/bundle.json","state":"https://pith.science/pith/I2MFC5PNFUMZMTZ5H6XYQMLEUP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I2MFC5PNFUMZMTZ5H6XYQMLEUP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:I2MFC5PNFUMZMTZ5H6XYQMLEUP","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":"ffefa970d0c5d00f6ed620dde45807a13770a9fc726d6ff523e183ef3037b6e8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-11T03:55:24Z","title_canon_sha256":"f1eaab1051e013500ccece3b52f3726655d5bb397656cf7ef4a8152f3ff2e6ad"},"schema_version":"1.0","source":{"id":"2403.06414","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.06414","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"arxiv_version","alias_value":"2403.06414v1","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06414","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"pith_short_12","alias_value":"I2MFC5PNFUMZ","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"pith_short_16","alias_value":"I2MFC5PNFUMZMTZ5","created_at":"2026-07-05T07:54:37Z"},{"alias_kind":"pith_short_8","alias_value":"I2MFC5PN","created_at":"2026-07-05T07:54:37Z"}],"graph_snapshots":[{"event_id":"sha256:15668cc6fe4f2c15a016543f1ba1aeeb5308f76becc89308b591898e941a7497","target":"graph","created_at":"2026-07-05T07:54:37Z","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/2403.06414/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. Nonetheless, these works have mainly focused on the direct use of LLMs for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of ac","authors_text":"Changlong Sun, Chengyuan Liu, Fei Wu, Fubang Zhao, Kun Kuang, Yangyang Kang, Zhuoren Jiang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-11T03:55:24Z","title":"Evolving Knowledge Distillation with Large Language Models and Active Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06414","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":"sha256:715505d20e72d8b61bf5cfc0a8d38cf1b2f3c3541e30809ba4315c74dd1bf599","target":"record","created_at":"2026-07-05T07:54:37Z","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":"ffefa970d0c5d00f6ed620dde45807a13770a9fc726d6ff523e183ef3037b6e8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-11T03:55:24Z","title_canon_sha256":"f1eaab1051e013500ccece3b52f3726655d5bb397656cf7ef4a8152f3ff2e6ad"},"schema_version":"1.0","source":{"id":"2403.06414","kind":"arxiv","version":1}},"canonical_sha256":"46985175ed2d19964f3d3faf883164a3e3f24a8d9bc4c49799b4b3be3bbc3868","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46985175ed2d19964f3d3faf883164a3e3f24a8d9bc4c49799b4b3be3bbc3868","first_computed_at":"2026-07-05T07:54:37.765683Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:54:37.765683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wl92qzoquJ7Ph2OFt9mzMJibx+x7tqMlAZ5wNWOk5InVNkpD2oUWVyn3/fDsl+nYYxNL3JfOq0vlQA/E6NbWCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:54:37.766143Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.06414","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:715505d20e72d8b61bf5cfc0a8d38cf1b2f3c3541e30809ba4315c74dd1bf599","sha256:15668cc6fe4f2c15a016543f1ba1aeeb5308f76becc89308b591898e941a7497"],"state_sha256":"daf7e7667d300f9c40621df646743a61b4fd32c97da03bb7da8659c157fc4da9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zys83BF1Us3ct5gTnarmCN2MIWjeIQaKoPa5pkEEEiUgBousrE21EK9WtYfXbYLlkiOb6s8fmt6GkJWXxNTKBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T18:03:04.776724Z","bundle_sha256":"a5a9d3e45bc5c9e405807bc74e7437daa59d940348b7dea65e12ccdb28343feb"}}