{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:TPBXPNSY26UZB34JCMBJUCRTKW","short_pith_number":"pith:TPBXPNSY","canonical_record":{"source":{"id":"2411.14064","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T12:26:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"26d7f70dbd871b327aa791e451d53c532bc6091c2d1580bd930b7bf16938f7be","abstract_canon_sha256":"9adc6ce262a0320297de98bb417d2cbf9448831bc96afb67643df9db9e2b0466"},"schema_version":"1.0"},"canonical_sha256":"9bc377b658d7a990ef8913029a0a3355aea71d3d0c853dd7fd2f083c748b575b","source":{"kind":"arxiv","id":"2411.14064","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.14064","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"arxiv_version","alias_value":"2411.14064v1","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14064","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"pith_short_12","alias_value":"TPBXPNSY26UZ","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"pith_short_16","alias_value":"TPBXPNSY26UZB34J","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"pith_short_8","alias_value":"TPBXPNSY","created_at":"2026-07-05T09:38:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:TPBXPNSY26UZB34JCMBJUCRTKW","target":"record","payload":{"canonical_record":{"source":{"id":"2411.14064","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T12:26:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"26d7f70dbd871b327aa791e451d53c532bc6091c2d1580bd930b7bf16938f7be","abstract_canon_sha256":"9adc6ce262a0320297de98bb417d2cbf9448831bc96afb67643df9db9e2b0466"},"schema_version":"1.0"},"canonical_sha256":"9bc377b658d7a990ef8913029a0a3355aea71d3d0c853dd7fd2f083c748b575b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:41.181039Z","signature_b64":"9a4rSmYqrr53rpO8itKhfJbotR88+1JPMSeHjuCajtgSXwji0MDxjNx1uKsEI0tefZ08I8BDXGHkQFVHRBJzBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bc377b658d7a990ef8913029a0a3355aea71d3d0c853dd7fd2f083c748b575b","last_reissued_at":"2026-07-05T09:38:41.180605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:41.180605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.14064","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":"2026-07-05T09:38:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ohbQGDFWPLVeRLuznjeFh6iTZUpSzf+IJluzCFPcGtGLHaQbuDkdcYx2yhLL2H8YNLTRPWYQps6S/tn081sIAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:56:53.284426Z"},"content_sha256":"3ba857ed5a6380c75ea25c7621101077f068bb698a50f1186711e4c50985787d","schema_version":"1.0","event_id":"sha256:3ba857ed5a6380c75ea25c7621101077f068bb698a50f1186711e4c50985787d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:TPBXPNSY26UZB34JCMBJUCRTKW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi LoRA Meets Vision: Merging multiple adapters to create a multi task model","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ege Kesim, Selahattin Serdar Helli","submitted_at":"2024-11-21T12:26:33Z","abstract_excerpt":"Parameter efficient finetuning (PEFT) methods are widely used in LLMs and generative models in computer vision. Especially one can use multiple of these during inference to change the behavior of the base model. In this paper we investigated whether multiple LoRA adapters trained on computer vision tasks can be merged together and used during inference without loss in performance. By achieving this, multitask models can be created just by merging different LoRAs. Merging these will reduce inference time and it will not require any additional retraining. We have trained adapters on six differen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14064","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/2411.14064/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":"2026-07-05T09:38:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4KG06SHTXypf/Hp83Y4MkY9a7DXk1sQydxor7Un4miSdl1Hg+okIiN3TFMrnWVUINRR0L3QD+H/cvvWZwl2QCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T15:56:53.285289Z"},"content_sha256":"6b0502b8d6d423c14d6d582f63df87c0b9f1ab7a7e72ff22ff3afae7d61956b9","schema_version":"1.0","event_id":"sha256:6b0502b8d6d423c14d6d582f63df87c0b9f1ab7a7e72ff22ff3afae7d61956b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TPBXPNSY26UZB34JCMBJUCRTKW/bundle.json","state_url":"https://pith.science/pith/TPBXPNSY26UZB34JCMBJUCRTKW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TPBXPNSY26UZB34JCMBJUCRTKW/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":"2026-08-05T15:56:53Z","links":{"resolver":"https://pith.science/pith/TPBXPNSY26UZB34JCMBJUCRTKW","bundle":"https://pith.science/pith/TPBXPNSY26UZB34JCMBJUCRTKW/bundle.json","state":"https://pith.science/pith/TPBXPNSY26UZB34JCMBJUCRTKW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TPBXPNSY26UZB34JCMBJUCRTKW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TPBXPNSY26UZB34JCMBJUCRTKW","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":"9adc6ce262a0320297de98bb417d2cbf9448831bc96afb67643df9db9e2b0466","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T12:26:33Z","title_canon_sha256":"26d7f70dbd871b327aa791e451d53c532bc6091c2d1580bd930b7bf16938f7be"},"schema_version":"1.0","source":{"id":"2411.14064","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.14064","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"arxiv_version","alias_value":"2411.14064v1","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14064","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"pith_short_12","alias_value":"TPBXPNSY26UZ","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"pith_short_16","alias_value":"TPBXPNSY26UZB34J","created_at":"2026-07-05T09:38:41Z"},{"alias_kind":"pith_short_8","alias_value":"TPBXPNSY","created_at":"2026-07-05T09:38:41Z"}],"graph_snapshots":[{"event_id":"sha256:6b0502b8d6d423c14d6d582f63df87c0b9f1ab7a7e72ff22ff3afae7d61956b9","target":"graph","created_at":"2026-07-05T09:38:41Z","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/2411.14064/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Parameter efficient finetuning (PEFT) methods are widely used in LLMs and generative models in computer vision. Especially one can use multiple of these during inference to change the behavior of the base model. In this paper we investigated whether multiple LoRA adapters trained on computer vision tasks can be merged together and used during inference without loss in performance. By achieving this, multitask models can be created just by merging different LoRAs. Merging these will reduce inference time and it will not require any additional retraining. We have trained adapters on six differen","authors_text":"Ege Kesim, Selahattin Serdar Helli","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T12:26:33Z","title":"Multi LoRA Meets Vision: Merging multiple adapters to create a multi task model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14064","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":"sha256:3ba857ed5a6380c75ea25c7621101077f068bb698a50f1186711e4c50985787d","target":"record","created_at":"2026-07-05T09:38:41Z","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":"9adc6ce262a0320297de98bb417d2cbf9448831bc96afb67643df9db9e2b0466","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T12:26:33Z","title_canon_sha256":"26d7f70dbd871b327aa791e451d53c532bc6091c2d1580bd930b7bf16938f7be"},"schema_version":"1.0","source":{"id":"2411.14064","kind":"arxiv","version":1}},"canonical_sha256":"9bc377b658d7a990ef8913029a0a3355aea71d3d0c853dd7fd2f083c748b575b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9bc377b658d7a990ef8913029a0a3355aea71d3d0c853dd7fd2f083c748b575b","first_computed_at":"2026-07-05T09:38:41.180605Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:38:41.180605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9a4rSmYqrr53rpO8itKhfJbotR88+1JPMSeHjuCajtgSXwji0MDxjNx1uKsEI0tefZ08I8BDXGHkQFVHRBJzBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:38:41.181039Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.14064","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ba857ed5a6380c75ea25c7621101077f068bb698a50f1186711e4c50985787d","sha256:6b0502b8d6d423c14d6d582f63df87c0b9f1ab7a7e72ff22ff3afae7d61956b9"],"state_sha256":"cf5469a07af149f76bc4093c9381ade38addebe931e75745a593de18006f5aa6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6dTdSltmO7PA424xq/zPXkVCKSBSVnSvzhr/FOkKV8L/yuS9sEewoYFzysXQevinAnGMJJew4RfdzT3B99HAAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T15:56:53.292180Z","bundle_sha256":"a6136a520ef1eae80e6d665fa58b32742d1c8d526a7b9c6fe4df14279316dac8"}}