{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:LBSPODWXWLGP6AYFTWC6TOYGS2","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":"d4c2ac5623c0b758077b9df8f92f2a7b8e70bf11ea48bb42f49f85d139ef411f","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-02T12:45:52Z","title_canon_sha256":"721a55b41780beef4e345a6a406c543b7bf1114301ff42f0e16b192e7a2912ce"},"schema_version":"1.0","source":{"id":"2410.01497","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01497","created_at":"2026-07-05T10:16:13Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01497v2","created_at":"2026-07-05T10:16:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01497","created_at":"2026-07-05T10:16:13Z"},{"alias_kind":"pith_short_12","alias_value":"LBSPODWXWLGP","created_at":"2026-07-05T10:16:13Z"},{"alias_kind":"pith_short_16","alias_value":"LBSPODWXWLGP6AYF","created_at":"2026-07-05T10:16:13Z"},{"alias_kind":"pith_short_8","alias_value":"LBSPODWX","created_at":"2026-07-05T10:16:13Z"}],"graph_snapshots":[{"event_id":"sha256:a4f9da30fdfeafe880d36c65a97c2f87a3d071a61d7079aa3d737ec5b7615795","target":"graph","created_at":"2026-07-05T10:16:13Z","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/2410.01497/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in Large Language Models (LLMs) have achieved robust performance across diverse tasks, but fine-tuning these models for specific domains remains resource-intensive. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) address this challenge by fine-tuning a small subset of parameters. However, existing methods for fusing multiple LoRAs lack dynamic fusion based on contextual inputs and often increase inference time due to token-level operations. We propose DLP-LoRA, a Dynamic Lightweight Plugin that employs a mini-MLP module with only 5M parameters","authors_text":"Ruizhe Li, Yuxuan Zhang","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-02T12:45:52Z","title":"DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01497","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:e76f145a0fcae3b42672e670a918c8c73654eede9c231756e7e794dd307234d9","target":"record","created_at":"2026-07-05T10:16:13Z","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":"d4c2ac5623c0b758077b9df8f92f2a7b8e70bf11ea48bb42f49f85d139ef411f","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-02T12:45:52Z","title_canon_sha256":"721a55b41780beef4e345a6a406c543b7bf1114301ff42f0e16b192e7a2912ce"},"schema_version":"1.0","source":{"id":"2410.01497","kind":"arxiv","version":2}},"canonical_sha256":"5864f70ed7b2ccff03059d85e9bb069698184100733918a7b9c4db86e84e82b4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5864f70ed7b2ccff03059d85e9bb069698184100733918a7b9c4db86e84e82b4","first_computed_at":"2026-07-05T10:16:13.250440Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:16:13.250440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uBxtkLiVKYG5bWzKeZcwgF/7C5FkPjczodQ+WcDui8EKmYA5+ZtBuMTpq+4JQJQT3Q/m8HR5e6YHhXj8zyVYAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:16:13.250956Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.01497","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e76f145a0fcae3b42672e670a918c8c73654eede9c231756e7e794dd307234d9","sha256:a4f9da30fdfeafe880d36c65a97c2f87a3d071a61d7079aa3d737ec5b7615795"],"state_sha256":"21eb65bc4872780a368aab50bd87d67a76f1cb878c92fbb270a088ee3f180d85"}