{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:KICSOYB3WBF745B3YERGH4RNEZ","short_pith_number":"pith:KICSOYB3","canonical_record":{"source":{"id":"2402.11455","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-18T04:41:25Z","cross_cats_sorted":[],"title_canon_sha256":"2ff2a049bcc08019309de56353f6cce5298a5677b51b75eb7c9fbf1535edc9d5","abstract_canon_sha256":"70600056e6cd46c321e751e3f11b663c48e4a73005e436dd2a6cd46d65006b61"},"schema_version":"1.0"},"canonical_sha256":"520527603bb04bfe743bc12263f22d2666b55972dc2604cc33a710c0daef6181","source":{"kind":"arxiv","id":"2402.11455","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.11455","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"arxiv_version","alias_value":"2402.11455v1","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11455","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"pith_short_12","alias_value":"KICSOYB3WBF7","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"pith_short_16","alias_value":"KICSOYB3WBF745B3","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"pith_short_8","alias_value":"KICSOYB3","created_at":"2026-07-05T07:46:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:KICSOYB3WBF745B3YERGH4RNEZ","target":"record","payload":{"canonical_record":{"source":{"id":"2402.11455","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-18T04:41:25Z","cross_cats_sorted":[],"title_canon_sha256":"2ff2a049bcc08019309de56353f6cce5298a5677b51b75eb7c9fbf1535edc9d5","abstract_canon_sha256":"70600056e6cd46c321e751e3f11b663c48e4a73005e436dd2a6cd46d65006b61"},"schema_version":"1.0"},"canonical_sha256":"520527603bb04bfe743bc12263f22d2666b55972dc2604cc33a710c0daef6181","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:39.262201Z","signature_b64":"rgtNYFEz8qChEpSAWwJ15llR16q66d9eM9/DutzIcFP1dttS2CdEOEBPsysTz68sJPWIt21J7KYpH4n1PSmnCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"520527603bb04bfe743bc12263f22d2666b55972dc2604cc33a710c0daef6181","last_reissued_at":"2026-07-05T07:46:39.261712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:39.261712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.11455","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":"2026-07-05T07:46:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b+xFf9rfsm43h6rSzrlLRLw10k7Zq4SdnXZ63y/CB57NPpjPcr/D/Hg9ipmNZp0VJc06k/V+MVyhpjQjT1x+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T13:13:01.621278Z"},"content_sha256":"9963b335957af0145b75518ab5fcd7296f6419eeea0ce2f6f0abc4c9ec7a4223","schema_version":"1.0","event_id":"sha256:9963b335957af0145b75518ab5fcd7296f6419eeea0ce2f6f0abc4c9ec7a4223"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:KICSOYB3WBF745B3YERGH4RNEZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bowen Ping, Hanqing Wang, Maosong Sun, Shuo Wang, Xu Han, Yun Chen, Zhiyuan Liu","submitted_at":"2024-02-18T04:41:25Z","abstract_excerpt":"LoRA employs lightweight modules to customize large language models (LLMs) for each downstream task or domain, where different learned additional modules represent diverse skills. Combining existing LoRAs to address new tasks can enhance the reusability of learned LoRAs, particularly beneficial for tasks with limited annotated data. Most prior works on LoRA combination primarily rely on task-level weights for each involved LoRA, making different examples and tokens share the same LoRA weights. However, in generative tasks, different tokens may necessitate diverse skills to manage. Taking the C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11455","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/2402.11455/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":"2026-07-05T07:46:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nU5G5Z2RRje8bfuyux3Q8TUD69630ZN9oKoV+teZbykevvWYQYde2IIiDcdfsmcX9J1jwIJ89nyiY8/2FAvyCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T13:13:01.621822Z"},"content_sha256":"6d59b6cfc9ca1377f2452869d29595333b23850c3782e126d31a343756e5cac5","schema_version":"1.0","event_id":"sha256:6d59b6cfc9ca1377f2452869d29595333b23850c3782e126d31a343756e5cac5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KICSOYB3WBF745B3YERGH4RNEZ/bundle.json","state_url":"https://pith.science/pith/KICSOYB3WBF745B3YERGH4RNEZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KICSOYB3WBF745B3YERGH4RNEZ/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":"2026-08-15T13:13:01Z","links":{"resolver":"https://pith.science/pith/KICSOYB3WBF745B3YERGH4RNEZ","bundle":"https://pith.science/pith/KICSOYB3WBF745B3YERGH4RNEZ/bundle.json","state":"https://pith.science/pith/KICSOYB3WBF745B3YERGH4RNEZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KICSOYB3WBF745B3YERGH4RNEZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:KICSOYB3WBF745B3YERGH4RNEZ","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":"70600056e6cd46c321e751e3f11b663c48e4a73005e436dd2a6cd46d65006b61","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-18T04:41:25Z","title_canon_sha256":"2ff2a049bcc08019309de56353f6cce5298a5677b51b75eb7c9fbf1535edc9d5"},"schema_version":"1.0","source":{"id":"2402.11455","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.11455","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"arxiv_version","alias_value":"2402.11455v1","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11455","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"pith_short_12","alias_value":"KICSOYB3WBF7","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"pith_short_16","alias_value":"KICSOYB3WBF745B3","created_at":"2026-07-05T07:46:39Z"},{"alias_kind":"pith_short_8","alias_value":"KICSOYB3","created_at":"2026-07-05T07:46:39Z"}],"graph_snapshots":[{"event_id":"sha256:6d59b6cfc9ca1377f2452869d29595333b23850c3782e126d31a343756e5cac5","target":"graph","created_at":"2026-07-05T07:46:39Z","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/2402.11455/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"LoRA employs lightweight modules to customize large language models (LLMs) for each downstream task or domain, where different learned additional modules represent diverse skills. Combining existing LoRAs to address new tasks can enhance the reusability of learned LoRAs, particularly beneficial for tasks with limited annotated data. Most prior works on LoRA combination primarily rely on task-level weights for each involved LoRA, making different examples and tokens share the same LoRA weights. However, in generative tasks, different tokens may necessitate diverse skills to manage. Taking the C","authors_text":"Bowen Ping, Hanqing Wang, Maosong Sun, Shuo Wang, Xu Han, Yun Chen, Zhiyuan Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-18T04:41:25Z","title":"LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11455","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":"sha256:9963b335957af0145b75518ab5fcd7296f6419eeea0ce2f6f0abc4c9ec7a4223","target":"record","created_at":"2026-07-05T07:46:39Z","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":"70600056e6cd46c321e751e3f11b663c48e4a73005e436dd2a6cd46d65006b61","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-18T04:41:25Z","title_canon_sha256":"2ff2a049bcc08019309de56353f6cce5298a5677b51b75eb7c9fbf1535edc9d5"},"schema_version":"1.0","source":{"id":"2402.11455","kind":"arxiv","version":1}},"canonical_sha256":"520527603bb04bfe743bc12263f22d2666b55972dc2604cc33a710c0daef6181","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"520527603bb04bfe743bc12263f22d2666b55972dc2604cc33a710c0daef6181","first_computed_at":"2026-07-05T07:46:39.261712Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:46:39.261712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rgtNYFEz8qChEpSAWwJ15llR16q66d9eM9/DutzIcFP1dttS2CdEOEBPsysTz68sJPWIt21J7KYpH4n1PSmnCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:46:39.262201Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.11455","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9963b335957af0145b75518ab5fcd7296f6419eeea0ce2f6f0abc4c9ec7a4223","sha256:6d59b6cfc9ca1377f2452869d29595333b23850c3782e126d31a343756e5cac5"],"state_sha256":"0efbbe0bd77c22151df80fc3109e3c45f18068f9c82747dfb26df3d68f588ec5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"65IeJER11QNA2WIjIjjYZBijqZi2fweGGXE0/Xr89gtXzHiiKlcZNP4ls/0i7ZjSeG4KToTsse9vt4BIhCpjBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T13:13:01.626532Z","bundle_sha256":"135f0cbfeeda0ce940826e8c234954e53575b892d78a1baf1740c5a8e91c9df7"}}