{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GEG4RDLRLSX4QRDWQ7FL5RZY35","short_pith_number":"pith:GEG4RDLR","schema_version":"1.0","canonical_sha256":"310dc88d715cafc8447687cabec738df5010f6cbe53473ac10495b69a218aabc","source":{"kind":"arxiv","id":"2506.00495","version":1},"attestation_state":"computed","paper":{"title":"FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Haobo Wang, Junbo Zhao, Lirong Gao, Xinyi Wang, Yiming Zhang","submitted_at":"2025-05-31T10:27:08Z","abstract_excerpt":"Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a widely adopted strategy for adapting pre-trained Large Language Models (LLMs) to downstream tasks, significantly reducing memory and computational costs. However, most existing PEFT techniques uniformly deploy LoRA adapters across all layers, disregarding the intrinsic heterogeneity of layer contributions and task-specific rank requirements. This uniform paradigm leads to redundant parameter allocation and suboptimal adaptation efficiency. To address these limitations, we propose FLoE, a novel PEFT framework that introduces two k"},"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":"2506.00495","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-31T10:27:08Z","cross_cats_sorted":["cs.CL","stat.ML"],"title_canon_sha256":"c039b3357145d361696adab019393aba21fecb152be3e34b308f6696da5c481c","abstract_canon_sha256":"7969624c45a95e1fdec48eac262cb145fcbe1bf83d63c4fa1a16ce90e3dedf53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:35.851187Z","signature_b64":"0uMK10k+gdwRmH85lh5ygEu4b2T0HOs7/KeAqnQ0HOn+4MMMJldaPIeLjUIaliDnkWeUHe9bPwQDmX94cbhzBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"310dc88d715cafc8447687cabec738df5010f6cbe53473ac10495b69a218aabc","last_reissued_at":"2026-07-05T11:13:35.850750Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:35.850750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Haobo Wang, Junbo Zhao, Lirong Gao, Xinyi Wang, Yiming Zhang","submitted_at":"2025-05-31T10:27:08Z","abstract_excerpt":"Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a widely adopted strategy for adapting pre-trained Large Language Models (LLMs) to downstream tasks, significantly reducing memory and computational costs. However, most existing PEFT techniques uniformly deploy LoRA adapters across all layers, disregarding the intrinsic heterogeneity of layer contributions and task-specific rank requirements. This uniform paradigm leads to redundant parameter allocation and suboptimal adaptation efficiency. To address these limitations, we propose FLoE, a novel PEFT framework that introduces two k"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00495","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/2506.00495/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":"2506.00495","created_at":"2026-07-05T11:13:35.850805+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00495v1","created_at":"2026-07-05T11:13:35.850805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00495","created_at":"2026-07-05T11:13:35.850805+00:00"},{"alias_kind":"pith_short_12","alias_value":"GEG4RDLRLSX4","created_at":"2026-07-05T11:13:35.850805+00:00"},{"alias_kind":"pith_short_16","alias_value":"GEG4RDLRLSX4QRDW","created_at":"2026-07-05T11:13:35.850805+00:00"},{"alias_kind":"pith_short_8","alias_value":"GEG4RDLR","created_at":"2026-07-05T11:13:35.850805+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06166","citing_title":"One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35","json":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35.json","graph_json":"https://pith.science/api/pith-number/GEG4RDLRLSX4QRDWQ7FL5RZY35/graph.json","events_json":"https://pith.science/api/pith-number/GEG4RDLRLSX4QRDWQ7FL5RZY35/events.json","paper":"https://pith.science/paper/GEG4RDLR"},"agent_actions":{"view_html":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35","download_json":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35.json","view_paper":"https://pith.science/paper/GEG4RDLR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00495&json=true","fetch_graph":"https://pith.science/api/pith-number/GEG4RDLRLSX4QRDWQ7FL5RZY35/graph.json","fetch_events":"https://pith.science/api/pith-number/GEG4RDLRLSX4QRDWQ7FL5RZY35/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35/action/storage_attestation","attest_author":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35/action/author_attestation","sign_citation":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35/action/citation_signature","submit_replication":"https://pith.science/pith/GEG4RDLRLSX4QRDWQ7FL5RZY35/action/replication_record"}},"created_at":"2026-07-05T11:13:35.850805+00:00","updated_at":"2026-07-05T11:13:35.850805+00:00"}