{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IG6FDZQ3EMLC5K4WBWQ2QUUBRI","short_pith_number":"pith:IG6FDZQ3","schema_version":"1.0","canonical_sha256":"41bc51e61b23162eab960da1a852818a32829a07051bf87b27f73526d7d9524a","source":{"kind":"arxiv","id":"2405.13053","version":3},"attestation_state":"computed","paper":{"title":"MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jingwei Xu, Junyu Lai, Yunpeng Huang","submitted_at":"2024-05-19T20:46:07Z","abstract_excerpt":"The pretrain+fine-tune paradigm is foundational for deploying large language models (LLMs) across various downstream applications. Within this framework, Low-Rank Adaptation (LoRA) stands out for its parameter-efficient fine-tuning (PEFT), producing numerous reusable task-specific LoRA adapters. However, this approach requires explicit task intention selection, posing challenges for autonomous task sensing and switching during inference with multiple existing LoRA adapters embedded in a single LLM. In this work, we introduce MeteoRA (Multiple-tasks embedded LoRA), a scalable and efficient fram"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.13053","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-19T20:46:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"932bf690d66d054bc814fb59872be795bd494e7473003bbe96a44b789bf6a81a","abstract_canon_sha256":"1e8aa2e18f77f69485ada03ff0dbb4d4bb7491ccf8efe5d4aec579e8f4b976a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:42.104409Z","signature_b64":"3Y60WFN/GXRvPJcI6M0IdEZgkGBMZ0DD2aguYi7i/kIRmsHFsD7woLgDy6nC7aGzjLFMVr8kz4XDJmpyq0vhAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41bc51e61b23162eab960da1a852818a32829a07051bf87b27f73526d7d9524a","last_reissued_at":"2026-07-05T09:17:42.103947Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:42.103947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jingwei Xu, Junyu Lai, Yunpeng Huang","submitted_at":"2024-05-19T20:46:07Z","abstract_excerpt":"The pretrain+fine-tune paradigm is foundational for deploying large language models (LLMs) across various downstream applications. Within this framework, Low-Rank Adaptation (LoRA) stands out for its parameter-efficient fine-tuning (PEFT), producing numerous reusable task-specific LoRA adapters. However, this approach requires explicit task intention selection, posing challenges for autonomous task sensing and switching during inference with multiple existing LoRA adapters embedded in a single LLM. In this work, we introduce MeteoRA (Multiple-tasks embedded LoRA), a scalable and efficient fram"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.13053","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/2405.13053/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.13053","created_at":"2026-07-05T09:17:42.104003+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.13053v3","created_at":"2026-07-05T09:17:42.104003+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.13053","created_at":"2026-07-05T09:17:42.104003+00:00"},{"alias_kind":"pith_short_12","alias_value":"IG6FDZQ3EMLC","created_at":"2026-07-05T09:17:42.104003+00:00"},{"alias_kind":"pith_short_16","alias_value":"IG6FDZQ3EMLC5K4W","created_at":"2026-07-05T09:17:42.104003+00:00"},{"alias_kind":"pith_short_8","alias_value":"IG6FDZQ3","created_at":"2026-07-05T09:17:42.104003+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31432","citing_title":"Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31432","citing_title":"Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2507.00029","citing_title":"LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19048","citing_title":"SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI","json":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI.json","graph_json":"https://pith.science/api/pith-number/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/graph.json","events_json":"https://pith.science/api/pith-number/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/events.json","paper":"https://pith.science/paper/IG6FDZQ3"},"agent_actions":{"view_html":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI","download_json":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI.json","view_paper":"https://pith.science/paper/IG6FDZQ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.13053&json=true","fetch_graph":"https://pith.science/api/pith-number/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/graph.json","fetch_events":"https://pith.science/api/pith-number/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/action/storage_attestation","attest_author":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/action/author_attestation","sign_citation":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/action/citation_signature","submit_replication":"https://pith.science/pith/IG6FDZQ3EMLC5K4WBWQ2QUUBRI/action/replication_record"}},"created_at":"2026-07-05T09:17:42.104003+00:00","updated_at":"2026-07-05T09:17:42.104003+00:00"}