{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:JDHJ3KPCJGIXNGNG2IL2563NOG","short_pith_number":"pith:JDHJ3KPC","canonical_record":{"source":{"id":"2501.17635","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-29T13:12:01Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"aa4cb1414edcb8a46778902ddda0ef684a1b2016b82ecac679b4b182f3967ddf","abstract_canon_sha256":"ecb7dce8423872a59382f3811ba33aaeee3177a04455a52a90e7d37083222fd1"},"schema_version":"1.0"},"canonical_sha256":"48ce9da9e249917699a6d217aefb6d7196445803366f4e8b638f8695de54f627","source":{"kind":"arxiv","id":"2501.17635","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.17635","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"arxiv_version","alias_value":"2501.17635v3","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17635","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"pith_short_12","alias_value":"JDHJ3KPCJGIX","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"pith_short_16","alias_value":"JDHJ3KPCJGIXNGNG","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"pith_short_8","alias_value":"JDHJ3KPC","created_at":"2026-07-05T11:32:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:JDHJ3KPCJGIXNGNG2IL2563NOG","target":"record","payload":{"canonical_record":{"source":{"id":"2501.17635","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-29T13:12:01Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"aa4cb1414edcb8a46778902ddda0ef684a1b2016b82ecac679b4b182f3967ddf","abstract_canon_sha256":"ecb7dce8423872a59382f3811ba33aaeee3177a04455a52a90e7d37083222fd1"},"schema_version":"1.0"},"canonical_sha256":"48ce9da9e249917699a6d217aefb6d7196445803366f4e8b638f8695de54f627","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:23.869475Z","signature_b64":"R90a1/fbcFj0F6Qwy4rmEg8naEFFhCJFLB0VmVc5C5QapTkXomFjCXYV74ZbCZVn4Gaxf594ZbWS7kdVl3dGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48ce9da9e249917699a6d217aefb6d7196445803366f4e8b638f8695de54f627","last_reissued_at":"2026-07-05T11:32:23.868957Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:23.868957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.17635","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:32:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+gshkZSFPdyz8g/yslmgrj9ajiMGUjowJ59UBONEGX07RNxg0OsnHXxBLFLF0xt6adGymp9rViwVPD7Nt+OcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:32:40.914175Z"},"content_sha256":"caa989ecf8d858cba3c6ca8823e58053acb9fa27aab83381839fc959fbfe2026","schema_version":"1.0","event_id":"sha256:caa989ecf8d858cba3c6ca8823e58053acb9fa27aab83381839fc959fbfe2026"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:JDHJ3KPCJGIXNGNG2IL2563NOG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"In-Context Meta LoRA Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.CL","authors_text":"Chenyu Zhang, Hao Tang, Hao Zhao, Jingcai Guo, Lei Li, Mengzhu Wang, Minxi Yan, Nicu Sebe, Siyu Chen, Wenjie Chen, Xinwei Long, Yang Liu, Yan Wang, Yihua Shao, Ziyang Yan","submitted_at":"2025-01-29T13:12:01Z","abstract_excerpt":"Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. However, in scenarios that involve multiple tasks, training a separate LoRA model for each one results in considerable inefficiency in terms of storage and inference. Moreover, existing parameter generation methods fail to capture the correlations among these tasks, making multi-task LoRA parameter generation challenging. To address these limitations, we propose In-Context Meta LoRA (ICM-LoRA), a novel approach that efficiently achieves task-specific customization of large language models (LLMs)."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17635","kind":"arxiv","version":3},"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/2501.17635/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":"2026-07-05T11:32:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E/SJqu0vT5a0Rb7G6euocBT3n0XJeMg33nEcK9Jypa813zXcJaLmSEUGtymkGAN5jcpDvARKxRVTdSS6xKj2CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T10:32:40.914716Z"},"content_sha256":"8ad8f2005eaf0a98d421d68701065f9cc5a102a067271560c805b06225c3623d","schema_version":"1.0","event_id":"sha256:8ad8f2005eaf0a98d421d68701065f9cc5a102a067271560c805b06225c3623d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JDHJ3KPCJGIXNGNG2IL2563NOG/bundle.json","state_url":"https://pith.science/pith/JDHJ3KPCJGIXNGNG2IL2563NOG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JDHJ3KPCJGIXNGNG2IL2563NOG/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":"2026-08-07T10:32:40Z","links":{"resolver":"https://pith.science/pith/JDHJ3KPCJGIXNGNG2IL2563NOG","bundle":"https://pith.science/pith/JDHJ3KPCJGIXNGNG2IL2563NOG/bundle.json","state":"https://pith.science/pith/JDHJ3KPCJGIXNGNG2IL2563NOG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JDHJ3KPCJGIXNGNG2IL2563NOG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JDHJ3KPCJGIXNGNG2IL2563NOG","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":"ecb7dce8423872a59382f3811ba33aaeee3177a04455a52a90e7d37083222fd1","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-29T13:12:01Z","title_canon_sha256":"aa4cb1414edcb8a46778902ddda0ef684a1b2016b82ecac679b4b182f3967ddf"},"schema_version":"1.0","source":{"id":"2501.17635","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.17635","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"arxiv_version","alias_value":"2501.17635v3","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.17635","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"pith_short_12","alias_value":"JDHJ3KPCJGIX","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"pith_short_16","alias_value":"JDHJ3KPCJGIXNGNG","created_at":"2026-07-05T11:32:23Z"},{"alias_kind":"pith_short_8","alias_value":"JDHJ3KPC","created_at":"2026-07-05T11:32:23Z"}],"graph_snapshots":[{"event_id":"sha256:8ad8f2005eaf0a98d421d68701065f9cc5a102a067271560c805b06225c3623d","target":"graph","created_at":"2026-07-05T11:32:23Z","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/2501.17635/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. However, in scenarios that involve multiple tasks, training a separate LoRA model for each one results in considerable inefficiency in terms of storage and inference. Moreover, existing parameter generation methods fail to capture the correlations among these tasks, making multi-task LoRA parameter generation challenging. To address these limitations, we propose In-Context Meta LoRA (ICM-LoRA), a novel approach that efficiently achieves task-specific customization of large language models (LLMs).","authors_text":"Chenyu Zhang, Hao Tang, Hao Zhao, Jingcai Guo, Lei Li, Mengzhu Wang, Minxi Yan, Nicu Sebe, Siyu Chen, Wenjie Chen, Xinwei Long, Yang Liu, Yan Wang, Yihua Shao, Ziyang Yan","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-29T13:12:01Z","title":"In-Context Meta LoRA Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.17635","kind":"arxiv","version":3},"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:caa989ecf8d858cba3c6ca8823e58053acb9fa27aab83381839fc959fbfe2026","target":"record","created_at":"2026-07-05T11:32:23Z","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":"ecb7dce8423872a59382f3811ba33aaeee3177a04455a52a90e7d37083222fd1","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-29T13:12:01Z","title_canon_sha256":"aa4cb1414edcb8a46778902ddda0ef684a1b2016b82ecac679b4b182f3967ddf"},"schema_version":"1.0","source":{"id":"2501.17635","kind":"arxiv","version":3}},"canonical_sha256":"48ce9da9e249917699a6d217aefb6d7196445803366f4e8b638f8695de54f627","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"48ce9da9e249917699a6d217aefb6d7196445803366f4e8b638f8695de54f627","first_computed_at":"2026-07-05T11:32:23.868957Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:32:23.868957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R90a1/fbcFj0F6Qwy4rmEg8naEFFhCJFLB0VmVc5C5QapTkXomFjCXYV74ZbCZVn4Gaxf594ZbWS7kdVl3dGDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:32:23.869475Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.17635","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:caa989ecf8d858cba3c6ca8823e58053acb9fa27aab83381839fc959fbfe2026","sha256:8ad8f2005eaf0a98d421d68701065f9cc5a102a067271560c805b06225c3623d"],"state_sha256":"b93c318c3b9f2b441819a57f4bcf4b8b36e45ea1790abaf0b4bcd8327669f3fa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Iok+f1vJZ6eH4ebZ3neL8Rw/pAuZUfUAKVViADRcsmjcsRfvvSr4hGBRtDuLK0kYd5QqTtBRvpNHlrssNZekCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T10:32:40.918600Z","bundle_sha256":"9e863877911da1ddf181d7c3d45674aca9260d7fe5a53e17368f0af0b68aa3ce"}}