{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NDQO6UZB2YRD4AMS5M5JC7SMVP","short_pith_number":"pith:NDQO6UZB","canonical_record":{"source":{"id":"2402.07721","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T15:34:56Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"4a08ebc35cb39d7ce0cf4b128953f286ae2e3c3acd78e8c7ccde8f0aa84b8f6f","abstract_canon_sha256":"fd70a8f66b7e35802bb447420db6009257caa4671101ee8ce8eac2961597ac54"},"schema_version":"1.0"},"canonical_sha256":"68e0ef5321d6223e0192eb3a917e4cabf4ea72a422b52f0d30ba9b65abc268a8","source":{"kind":"arxiv","id":"2402.07721","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.07721","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"arxiv_version","alias_value":"2402.07721v2","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07721","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"pith_short_12","alias_value":"NDQO6UZB2YRD","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"pith_short_16","alias_value":"NDQO6UZB2YRD4AMS","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"pith_short_8","alias_value":"NDQO6UZB","created_at":"2026-07-05T08:33:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NDQO6UZB2YRD4AMS5M5JC7SMVP","target":"record","payload":{"canonical_record":{"source":{"id":"2402.07721","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T15:34:56Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"4a08ebc35cb39d7ce0cf4b128953f286ae2e3c3acd78e8c7ccde8f0aa84b8f6f","abstract_canon_sha256":"fd70a8f66b7e35802bb447420db6009257caa4671101ee8ce8eac2961597ac54"},"schema_version":"1.0"},"canonical_sha256":"68e0ef5321d6223e0192eb3a917e4cabf4ea72a422b52f0d30ba9b65abc268a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:33:35.573907Z","signature_b64":"L9KL9pWbFX4znsQ4+HBaSHlmlFonLCWnqUrG/sVrlR2f/VfIlF+17JtyEKoTwQFfS1xazkbIxeB7DQJCM9ZFBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68e0ef5321d6223e0192eb3a917e4cabf4ea72a422b52f0d30ba9b65abc268a8","last_reissued_at":"2026-07-05T08:33:35.573420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:33:35.573420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.07721","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":"2026-07-05T08:33:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y16QrGiTeMbJbSxgzsDWMl3PUHUFKnHi2pkBXsfmkBA9j6j0pu7brmuCPP0e25ZCqyzKMQ6H6FxZpnmg9rY6Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:52:50.133053Z"},"content_sha256":"2b75c9b809c0b71a2bf72d8b82f077e082cbd48f2a8738df77de24fdece11390","schema_version":"1.0","event_id":"sha256:2b75c9b809c0b71a2bf72d8b82f077e082cbd48f2a8738df77de24fdece11390"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NDQO6UZB2YRD4AMS5M5JC7SMVP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Conghui Zhu, Hongyun Zhou, Muyun Yang, Tiejun Zhao, Wang Xu, Xiangyu Lu","submitted_at":"2024-02-12T15:34:56Z","abstract_excerpt":"Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method, it introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources. However, it still faces resource consumption challenges during training when scaling up to larger models. Most previous studies have tackled this issue by using pruning techniques, which involve removing LoRA parameters deemed unimportant. Nonetheless, these efforts only analyze LoRA parameter features to evaluate their importance, such as parameter count, size, and g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07721","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/2402.07721/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":"2026-07-05T08:33:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EeL/qnur/jq1zmAFn1SFkSgKAEQIr/DU0+gdnINyphAaAU9wBETJc4h+NJ4R4x+U2nm+NxLrYFiMeG1QAj5tBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:52:50.133548Z"},"content_sha256":"37af092f35e051d53d404908bbb1c27da1520334e6705474e9b6f2c54e267ca2","schema_version":"1.0","event_id":"sha256:37af092f35e051d53d404908bbb1c27da1520334e6705474e9b6f2c54e267ca2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NDQO6UZB2YRD4AMS5M5JC7SMVP/bundle.json","state_url":"https://pith.science/pith/NDQO6UZB2YRD4AMS5M5JC7SMVP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NDQO6UZB2YRD4AMS5M5JC7SMVP/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":"2026-08-07T16:52:50Z","links":{"resolver":"https://pith.science/pith/NDQO6UZB2YRD4AMS5M5JC7SMVP","bundle":"https://pith.science/pith/NDQO6UZB2YRD4AMS5M5JC7SMVP/bundle.json","state":"https://pith.science/pith/NDQO6UZB2YRD4AMS5M5JC7SMVP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NDQO6UZB2YRD4AMS5M5JC7SMVP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NDQO6UZB2YRD4AMS5M5JC7SMVP","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":"fd70a8f66b7e35802bb447420db6009257caa4671101ee8ce8eac2961597ac54","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T15:34:56Z","title_canon_sha256":"4a08ebc35cb39d7ce0cf4b128953f286ae2e3c3acd78e8c7ccde8f0aa84b8f6f"},"schema_version":"1.0","source":{"id":"2402.07721","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.07721","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"arxiv_version","alias_value":"2402.07721v2","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07721","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"pith_short_12","alias_value":"NDQO6UZB2YRD","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"pith_short_16","alias_value":"NDQO6UZB2YRD4AMS","created_at":"2026-07-05T08:33:35Z"},{"alias_kind":"pith_short_8","alias_value":"NDQO6UZB","created_at":"2026-07-05T08:33:35Z"}],"graph_snapshots":[{"event_id":"sha256:37af092f35e051d53d404908bbb1c27da1520334e6705474e9b6f2c54e267ca2","target":"graph","created_at":"2026-07-05T08:33:35Z","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/2402.07721/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method, it introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources. However, it still faces resource consumption challenges during training when scaling up to larger models. Most previous studies have tackled this issue by using pruning techniques, which involve removing LoRA parameters deemed unimportant. Nonetheless, these efforts only analyze LoRA parameter features to evaluate their importance, such as parameter count, size, and g","authors_text":"Conghui Zhu, Hongyun Zhou, Muyun Yang, Tiejun Zhao, Wang Xu, Xiangyu Lu","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T15:34:56Z","title":"LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07721","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":"sha256:2b75c9b809c0b71a2bf72d8b82f077e082cbd48f2a8738df77de24fdece11390","target":"record","created_at":"2026-07-05T08:33:35Z","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":"fd70a8f66b7e35802bb447420db6009257caa4671101ee8ce8eac2961597ac54","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T15:34:56Z","title_canon_sha256":"4a08ebc35cb39d7ce0cf4b128953f286ae2e3c3acd78e8c7ccde8f0aa84b8f6f"},"schema_version":"1.0","source":{"id":"2402.07721","kind":"arxiv","version":2}},"canonical_sha256":"68e0ef5321d6223e0192eb3a917e4cabf4ea72a422b52f0d30ba9b65abc268a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"68e0ef5321d6223e0192eb3a917e4cabf4ea72a422b52f0d30ba9b65abc268a8","first_computed_at":"2026-07-05T08:33:35.573420Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:33:35.573420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L9KL9pWbFX4znsQ4+HBaSHlmlFonLCWnqUrG/sVrlR2f/VfIlF+17JtyEKoTwQFfS1xazkbIxeB7DQJCM9ZFBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:33:35.573907Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.07721","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b75c9b809c0b71a2bf72d8b82f077e082cbd48f2a8738df77de24fdece11390","sha256:37af092f35e051d53d404908bbb1c27da1520334e6705474e9b6f2c54e267ca2"],"state_sha256":"e04e3a8c1ac27b24644c410e7a93942ae2723467c12a62aafd921d03ffe4ee94"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e4NgbSuKAMo1BcMBO3diVQtUpnHiD+ivWmV+oV3q2eYHBNHCeKqOZfYhEApq0GtEE2UMw3hgbfUZG3TxDBlRCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T16:52:50.138303Z","bundle_sha256":"a7e40b759c785d9c500341544c9f044d70eb6418a2c222b076fcf3fa92d13fd2"}}