{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DP3HPN57BTGMINZHNGPZNWJRE4","short_pith_number":"pith:DP3HPN57","canonical_record":{"source":{"id":"2510.24561","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-10-28T15:55:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b811800cce06bd1d870a26acdd53dc356f65eff51563678282744256ac902ec7","abstract_canon_sha256":"f18d6e330c036d5df6c7ececba4202299bf8cb899ec23d89e25de73155c45295"},"schema_version":"1.0"},"canonical_sha256":"1bf677b7bf0cccc43727699f96d93127054d573cf321ee2b537bded353e506f1","source":{"kind":"arxiv","id":"2510.24561","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.24561","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"arxiv_version","alias_value":"2510.24561v3","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.24561","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"pith_short_12","alias_value":"DP3HPN57BTGM","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"pith_short_16","alias_value":"DP3HPN57BTGMINZH","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"pith_short_8","alias_value":"DP3HPN57","created_at":"2026-06-08T01:03:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DP3HPN57BTGMINZHNGPZNWJRE4","target":"record","payload":{"canonical_record":{"source":{"id":"2510.24561","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-10-28T15:55:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b811800cce06bd1d870a26acdd53dc356f65eff51563678282744256ac902ec7","abstract_canon_sha256":"f18d6e330c036d5df6c7ececba4202299bf8cb899ec23d89e25de73155c45295"},"schema_version":"1.0"},"canonical_sha256":"1bf677b7bf0cccc43727699f96d93127054d573cf321ee2b537bded353e506f1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:03:50.387066Z","signature_b64":"A4glQSZABUa5I8r3B2gvhHTEuTb5GdbbQrRrfzHUrk2jMtm5OOVeesv5ep4+PuVutGuquLiJYlRT5DqYM3OXAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1bf677b7bf0cccc43727699f96d93127054d573cf321ee2b537bded353e506f1","last_reissued_at":"2026-06-08T01:03:50.386093Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:03:50.386093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.24561","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":"2026-06-08T01:03:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SFMEGryjh2+WWwP1ppnPZDCvfC8nLaP2EfHB6VpTHv0PGh7XKhJrGxF2fppvUL3pAbrYYkyMZFwHLe5hrJ1nDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:27:38.644689Z"},"content_sha256":"393870ce6154fa2d9990f5831aeca39c4c2c556f294b9300bc3442137526f831","schema_version":"1.0","event_id":"sha256:393870ce6154fa2d9990f5831aeca39c4c2c556f294b9300bc3442137526f831"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DP3HPN57BTGMINZHNGPZNWJRE4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Minimizing the expected parameter discrepancy between fine-tuned and target models yields an optimal data-aware initialization for LoRA.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Chang Chu, Qi Li, Qingyue Zhang, Shao-Lun Huang, Tianren Peng, Xiangyang Luo, Zhihao Jiang","submitted_at":"2025-10-28T15:55:36Z","abstract_excerpt":"LoRA has become a widely adopted method for PEFT, and its initialization methods have attracted increasing attention. However, existing methods have notable limitations: many methods do not incorporate target-domain data, while gradient-based methods exploit data only at a shallow level by relying on one-step gradient decomposition. In this paper, we establish a theoretical framework for data-aware LoRA initialization. Starting from minimizing the expectation of the parameter discrepancy between the fine-tuned and target models, we derive an optimization problem with two components: a bias ter"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Solving this problem yields an optimal initialization strategy for LoRA, based on which we develop an efficient algorithm, LoRA-DA. Empirical results across multiple benchmarks demonstrate that LoRA-DA consistently improves final accuracy over existing initialization methods.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The bias term is approximated using a Fisher-gradient formulation to preserve anisotropy while the variance term uses the Fisher information to capture sampling uncertainty; this approximation must hold for the derived initialization to be optimal in the target fine-tuning regime.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"LoRA-DA derives an optimal data-aware LoRA initialization by solving an optimization problem from asymptotic analysis of parameter discrepancy using Fisher-gradient bias and Fisher-information variance terms.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Minimizing the expected parameter discrepancy between fine-tuned and target models yields an optimal data-aware initialization for LoRA.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f5d2579ad610b677589cf8e06b4f1aed8c7c49a51d09d0b2e63ae1edf92b9883"},"source":{"id":"2510.24561","kind":"arxiv","version":3},"verdict":{"id":"dfbdd8dc-eae1-4469-959a-c0a7071af2a3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T02:58:14.357911Z","strongest_claim":"Solving this problem yields an optimal initialization strategy for LoRA, based on which we develop an efficient algorithm, LoRA-DA. Empirical results across multiple benchmarks demonstrate that LoRA-DA consistently improves final accuracy over existing initialization methods.","one_line_summary":"LoRA-DA derives an optimal data-aware LoRA initialization by solving an optimization problem from asymptotic analysis of parameter discrepancy using Fisher-gradient bias and Fisher-information variance terms.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The bias term is approximated using a Fisher-gradient formulation to preserve anisotropy while the variance term uses the Fisher information to capture sampling uncertainty; this approximation must hold for the derived initialization to be optimal in the target fine-tuning regime.","pith_extraction_headline":"Minimizing the expected parameter discrepancy between fine-tuned and target models yields an optimal data-aware initialization for LoRA."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.24561/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":2,"snapshot_sha256":"7f21efc0f643fe16f221e875a524f596f1cdad16260413e46ef1097ae2074a7a"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"dfbdd8dc-eae1-4469-959a-c0a7071af2a3"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-08T01:03:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8HyS6WKqqa3+RkyCp7JkxcmBxLu8O4sVx6bc68Ctcp2mdqjtNK7QyBrLjGOon5fGl1g4+abmGMpVTyPpy1+wCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T21:27:38.645161Z"},"content_sha256":"87c7f2b002c58cd9408ce5e64c18b047bfcbe8cb712183a8094d3b7451faa254","schema_version":"1.0","event_id":"sha256:87c7f2b002c58cd9408ce5e64c18b047bfcbe8cb712183a8094d3b7451faa254"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DP3HPN57BTGMINZHNGPZNWJRE4/bundle.json","state_url":"https://pith.science/pith/DP3HPN57BTGMINZHNGPZNWJRE4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DP3HPN57BTGMINZHNGPZNWJRE4/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":"2026-06-29T21:27:38Z","links":{"resolver":"https://pith.science/pith/DP3HPN57BTGMINZHNGPZNWJRE4","bundle":"https://pith.science/pith/DP3HPN57BTGMINZHNGPZNWJRE4/bundle.json","state":"https://pith.science/pith/DP3HPN57BTGMINZHNGPZNWJRE4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DP3HPN57BTGMINZHNGPZNWJRE4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DP3HPN57BTGMINZHNGPZNWJRE4","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":"f18d6e330c036d5df6c7ececba4202299bf8cb899ec23d89e25de73155c45295","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-10-28T15:55:36Z","title_canon_sha256":"b811800cce06bd1d870a26acdd53dc356f65eff51563678282744256ac902ec7"},"schema_version":"1.0","source":{"id":"2510.24561","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.24561","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"arxiv_version","alias_value":"2510.24561v3","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.24561","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"pith_short_12","alias_value":"DP3HPN57BTGM","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"pith_short_16","alias_value":"DP3HPN57BTGMINZH","created_at":"2026-06-08T01:03:50Z"},{"alias_kind":"pith_short_8","alias_value":"DP3HPN57","created_at":"2026-06-08T01:03:50Z"}],"graph_snapshots":[{"event_id":"sha256:87c7f2b002c58cd9408ce5e64c18b047bfcbe8cb712183a8094d3b7451faa254","target":"graph","created_at":"2026-06-08T01:03:50Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Solving this problem yields an optimal initialization strategy for LoRA, based on which we develop an efficient algorithm, LoRA-DA. Empirical results across multiple benchmarks demonstrate that LoRA-DA consistently improves final accuracy over existing initialization methods."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The bias term is approximated using a Fisher-gradient formulation to preserve anisotropy while the variance term uses the Fisher information to capture sampling uncertainty; this approximation must hold for the derived initialization to be optimal in the target fine-tuning regime."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"LoRA-DA derives an optimal data-aware LoRA initialization by solving an optimization problem from asymptotic analysis of parameter discrepancy using Fisher-gradient bias and Fisher-information variance terms."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Minimizing the expected parameter discrepancy between fine-tuned and target models yields an optimal data-aware initialization for LoRA."}],"snapshot_sha256":"f5d2579ad610b677589cf8e06b4f1aed8c7c49a51d09d0b2e63ae1edf92b9883"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"7f21efc0f643fe16f221e875a524f596f1cdad16260413e46ef1097ae2074a7a"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2510.24561/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"LoRA has become a widely adopted method for PEFT, and its initialization methods have attracted increasing attention. However, existing methods have notable limitations: many methods do not incorporate target-domain data, while gradient-based methods exploit data only at a shallow level by relying on one-step gradient decomposition. In this paper, we establish a theoretical framework for data-aware LoRA initialization. Starting from minimizing the expectation of the parameter discrepancy between the fine-tuned and target models, we derive an optimization problem with two components: a bias ter","authors_text":"Chang Chu, Qi Li, Qingyue Zhang, Shao-Lun Huang, Tianren Peng, Xiangyang Luo, Zhihao Jiang","cross_cats":["cs.AI"],"headline":"Minimizing the expected parameter discrepancy between fine-tuned and target models yields an optimal data-aware initialization for LoRA.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-10-28T15:55:36Z","title":"LoRA-DA: Data-Aware Initialization for Low-Rank Adaptation via Asymptotic Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.24561","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-18T02:58:14.357911Z","id":"dfbdd8dc-eae1-4469-959a-c0a7071af2a3","model_set":{"reader":"grok-4.3"},"one_line_summary":"LoRA-DA derives an optimal data-aware LoRA initialization by solving an optimization problem from asymptotic analysis of parameter discrepancy using Fisher-gradient bias and Fisher-information variance terms.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Minimizing the expected parameter discrepancy between fine-tuned and target models yields an optimal data-aware initialization for LoRA.","strongest_claim":"Solving this problem yields an optimal initialization strategy for LoRA, based on which we develop an efficient algorithm, LoRA-DA. Empirical results across multiple benchmarks demonstrate that LoRA-DA consistently improves final accuracy over existing initialization methods.","weakest_assumption":"The bias term is approximated using a Fisher-gradient formulation to preserve anisotropy while the variance term uses the Fisher information to capture sampling uncertainty; this approximation must hold for the derived initialization to be optimal in the target fine-tuning regime."}},"verdict_id":"dfbdd8dc-eae1-4469-959a-c0a7071af2a3"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:393870ce6154fa2d9990f5831aeca39c4c2c556f294b9300bc3442137526f831","target":"record","created_at":"2026-06-08T01:03:50Z","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":"f18d6e330c036d5df6c7ececba4202299bf8cb899ec23d89e25de73155c45295","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-10-28T15:55:36Z","title_canon_sha256":"b811800cce06bd1d870a26acdd53dc356f65eff51563678282744256ac902ec7"},"schema_version":"1.0","source":{"id":"2510.24561","kind":"arxiv","version":3}},"canonical_sha256":"1bf677b7bf0cccc43727699f96d93127054d573cf321ee2b537bded353e506f1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1bf677b7bf0cccc43727699f96d93127054d573cf321ee2b537bded353e506f1","first_computed_at":"2026-06-08T01:03:50.386093Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:03:50.386093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A4glQSZABUa5I8r3B2gvhHTEuTb5GdbbQrRrfzHUrk2jMtm5OOVeesv5ep4+PuVutGuquLiJYlRT5DqYM3OXAQ==","signature_status":"signed_v1","signed_at":"2026-06-08T01:03:50.387066Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.24561","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:393870ce6154fa2d9990f5831aeca39c4c2c556f294b9300bc3442137526f831","sha256:87c7f2b002c58cd9408ce5e64c18b047bfcbe8cb712183a8094d3b7451faa254"],"state_sha256":"0fc558b0add56ce5c0f57af0bbce7291ae9dbda06d997069e66303f0d67df169"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pV/K1l0NI8RRmC0gUk/w7l7a+K6JIoaGSO5rLUaGcDW/lFP4g3C0Su93tNV8PNYn1WlmKpLrVTFu8S0N+Wh1DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T21:27:38.647459Z","bundle_sha256":"ff66298871f19e23ef6577f09a4fb9ff54987e1274b78d529a87e07b74fd10d9"}}