{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TMZOORUZ33LLIPMNOYSK4UJLG5","short_pith_number":"pith:TMZOORUZ","canonical_record":{"source":{"id":"2605.21147","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-20T13:19:28Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"285fb80d0bcd1b9336dbad8e5edfcf8e72595ccad1adb535eb224b68f8a57ed9","abstract_canon_sha256":"8ec043997fed07ed3f617b1a915e1f2a7aa2c53738f77fc232c3d4587189808b"},"schema_version":"1.0"},"canonical_sha256":"9b32e74699ded6b43d8d7624ae512b376171b9ba5cc603f42c9d1cd7649b0f0b","source":{"kind":"arxiv","id":"2605.21147","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21147","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21147v1","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21147","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_12","alias_value":"TMZOORUZ33LL","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_16","alias_value":"TMZOORUZ33LLIPMN","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_8","alias_value":"TMZOORUZ","created_at":"2026-05-21T01:05:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TMZOORUZ33LLIPMNOYSK4UJLG5","target":"record","payload":{"canonical_record":{"source":{"id":"2605.21147","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-20T13:19:28Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"285fb80d0bcd1b9336dbad8e5edfcf8e72595ccad1adb535eb224b68f8a57ed9","abstract_canon_sha256":"8ec043997fed07ed3f617b1a915e1f2a7aa2c53738f77fc232c3d4587189808b"},"schema_version":"1.0"},"canonical_sha256":"9b32e74699ded6b43d8d7624ae512b376171b9ba5cc603f42c9d1cd7649b0f0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:39.962373Z","signature_b64":"6WZ0aSsfKpuyeO13jvpS23BUXdb+WzEaDq2I6a8rXBJnjFtyTP5TRwVDostTsJTxf/3tGM8vWP0ZnmEyToBcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b32e74699ded6b43d8d7624ae512b376171b9ba5cc603f42c9d1cd7649b0f0b","last_reissued_at":"2026-05-21T01:05:39.961533Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:39.961533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.21147","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-21T01:05:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O4eb7MfpYkEbr7/njZ0UElncORkZSq/mTsUZr2MipyOLSiflim1sLzCPAmwKxAYp49Yww4oP4LGQqN2G5/6+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T09:05:58.861926Z"},"content_sha256":"d5f4b2229fae12aef5833afa0890b79d4ef5195e2de5812613b0e7bb18b79c7c","schema_version":"1.0","event_id":"sha256:d5f4b2229fae12aef5833afa0890b79d4ef5195e2de5812613b0e7bb18b79c7c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TMZOORUZ33LLIPMNOYSK4UJLG5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Daling Wang, Feiliang Ren, Hinrich Sch\\\"utze, Mengjie Zhao, Qian Li, Shanru Zhang, Shi Feng, Xing Li, Yongkang Liu, Zijing Wang","submitted_at":"2026-05-20T13:19:28Z","abstract_excerpt":"As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation (LoRA) uses a low-rank update method to simulate full parameter fine-tuning, which is widely used to reduce resource requirements. However, decreasing the rank encounters challenges with limited representational capacity. Theory suggests that LoRA fine-tuning with rank r converges toward the top r singular values of the pre-trained weight matrix. As the rank increases, more principal singular directions are preserve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21147","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/2605.21147/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":"2026-05-21T01:05:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fwousigbaiIsbegTf1tLMtUaZhDhQLoYF54wDqivSmrlBizlq6w3lO0ZLadNe6hLGgPtftzaP0xfIkyvWVBdBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T09:05:58.862642Z"},"content_sha256":"2cb3d537b014a87f6611d854053bba3b6f3cde1ce2a80948b47691de668a306c","schema_version":"1.0","event_id":"sha256:2cb3d537b014a87f6611d854053bba3b6f3cde1ce2a80948b47691de668a306c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TMZOORUZ33LLIPMNOYSK4UJLG5/bundle.json","state_url":"https://pith.science/pith/TMZOORUZ33LLIPMNOYSK4UJLG5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TMZOORUZ33LLIPMNOYSK4UJLG5/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":"2026-05-22T09:05:58Z","links":{"resolver":"https://pith.science/pith/TMZOORUZ33LLIPMNOYSK4UJLG5","bundle":"https://pith.science/pith/TMZOORUZ33LLIPMNOYSK4UJLG5/bundle.json","state":"https://pith.science/pith/TMZOORUZ33LLIPMNOYSK4UJLG5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TMZOORUZ33LLIPMNOYSK4UJLG5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TMZOORUZ33LLIPMNOYSK4UJLG5","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":"8ec043997fed07ed3f617b1a915e1f2a7aa2c53738f77fc232c3d4587189808b","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-20T13:19:28Z","title_canon_sha256":"285fb80d0bcd1b9336dbad8e5edfcf8e72595ccad1adb535eb224b68f8a57ed9"},"schema_version":"1.0","source":{"id":"2605.21147","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21147","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21147v1","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21147","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_12","alias_value":"TMZOORUZ33LL","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_16","alias_value":"TMZOORUZ33LLIPMN","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_8","alias_value":"TMZOORUZ","created_at":"2026-05-21T01:05:39Z"}],"graph_snapshots":[{"event_id":"sha256:2cb3d537b014a87f6611d854053bba3b6f3cde1ce2a80948b47691de668a306c","target":"graph","created_at":"2026-05-21T01:05:39Z","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/2605.21147/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large language models. Low-rank Adaptation (LoRA) uses a low-rank update method to simulate full parameter fine-tuning, which is widely used to reduce resource requirements. However, decreasing the rank encounters challenges with limited representational capacity. Theory suggests that LoRA fine-tuning with rank r converges toward the top r singular values of the pre-trained weight matrix. As the rank increases, more principal singular directions are preserve","authors_text":"Daling Wang, Feiliang Ren, Hinrich Sch\\\"utze, Mengjie Zhao, Qian Li, Shanru Zhang, Shi Feng, Xing Li, Yongkang Liu, Zijing Wang","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-20T13:19:28Z","title":"SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21147","kind":"arxiv","version":1},"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:d5f4b2229fae12aef5833afa0890b79d4ef5195e2de5812613b0e7bb18b79c7c","target":"record","created_at":"2026-05-21T01:05:39Z","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":"8ec043997fed07ed3f617b1a915e1f2a7aa2c53738f77fc232c3d4587189808b","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-20T13:19:28Z","title_canon_sha256":"285fb80d0bcd1b9336dbad8e5edfcf8e72595ccad1adb535eb224b68f8a57ed9"},"schema_version":"1.0","source":{"id":"2605.21147","kind":"arxiv","version":1}},"canonical_sha256":"9b32e74699ded6b43d8d7624ae512b376171b9ba5cc603f42c9d1cd7649b0f0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b32e74699ded6b43d8d7624ae512b376171b9ba5cc603f42c9d1cd7649b0f0b","first_computed_at":"2026-05-21T01:05:39.961533Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-21T01:05:39.961533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6WZ0aSsfKpuyeO13jvpS23BUXdb+WzEaDq2I6a8rXBJnjFtyTP5TRwVDostTsJTxf/3tGM8vWP0ZnmEyToBcBA==","signature_status":"signed_v1","signed_at":"2026-05-21T01:05:39.962373Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.21147","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d5f4b2229fae12aef5833afa0890b79d4ef5195e2de5812613b0e7bb18b79c7c","sha256:2cb3d537b014a87f6611d854053bba3b6f3cde1ce2a80948b47691de668a306c"],"state_sha256":"aa5cbf2a7926e727586da7642394fdb3cd544754753c1a9288a55851cb065681"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0UQ5rU+jiTrAbyW/2xsyj6t00dv7IU2J5inUvL87St4//jD+eGhP5U3P8DltaQ/h9Y39wMroZLk6HXPO+5DGCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T09:05:58.865986Z","bundle_sha256":"d5f722ec42f9f70392347e22dc6ea72f181f8d2b42b4f3a505e7d4932487cc82"}}