{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:IMA4QIUMVIDUIRVIOJME56TA5K","short_pith_number":"pith:IMA4QIUM","canonical_record":{"source":{"id":"2606.10464","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-06-09T06:29:13Z","cross_cats_sorted":[],"title_canon_sha256":"69f7e2dc06be14f59ae993249823c3e94675f8583b1ffcc3e846ef9d974f8e6f","abstract_canon_sha256":"dbc12b27c7160a16578ffa1449468d55054cb719e7bafb8169ab613831b992ea"},"schema_version":"1.0"},"canonical_sha256":"4301c8228caa074446a872584efa60eaa88f59721560727ea5542e14b3699e44","source":{"kind":"arxiv","id":"2606.10464","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.10464","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"arxiv_version","alias_value":"2606.10464v1","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10464","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"pith_short_12","alias_value":"IMA4QIUMVIDU","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"pith_short_16","alias_value":"IMA4QIUMVIDUIRVI","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"pith_short_8","alias_value":"IMA4QIUM","created_at":"2026-06-10T01:10:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:IMA4QIUMVIDUIRVIOJME56TA5K","target":"record","payload":{"canonical_record":{"source":{"id":"2606.10464","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-06-09T06:29:13Z","cross_cats_sorted":[],"title_canon_sha256":"69f7e2dc06be14f59ae993249823c3e94675f8583b1ffcc3e846ef9d974f8e6f","abstract_canon_sha256":"dbc12b27c7160a16578ffa1449468d55054cb719e7bafb8169ab613831b992ea"},"schema_version":"1.0"},"canonical_sha256":"4301c8228caa074446a872584efa60eaa88f59721560727ea5542e14b3699e44","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:10:20.575001Z","signature_b64":"PKT7zScAaQA8d7XByhymr0lzbsx+FxxO80954H0ECvgfC1usy3UsxW1oax/xuRqpsvFq92zGXmhlH0ax3cPxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4301c8228caa074446a872584efa60eaa88f59721560727ea5542e14b3699e44","last_reissued_at":"2026-06-10T01:10:20.574209Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:10:20.574209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.10464","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":"2026-06-10T01:10:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nnYlTxjo2ucPhq6rzyO7jy2UBDmMbcJ2UjiEEXJjYEVSXHf5lHyDyKFdBjDPFf1tY4OuSlbH65dCnQLBoLN6AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T02:01:01.039453Z"},"content_sha256":"1ea67296aaf6ce250bff4f6a09feaed5f10fcd5892fa0ad5380e9916b11a0440","schema_version":"1.0","event_id":"sha256:1ea67296aaf6ce250bff4f6a09feaed5f10fcd5892fa0ad5380e9916b11a0440"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:IMA4QIUMVIDUIRVIOJME56TA5K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GC-LoRA: Gated Convolutional LoRA for Parameter-Efficient Acoustic Adaptation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Abeer Alwan, Kaiyuan Zhang, Mohan Shi, Natarajan Balaji Shankar, Zilai Wang","submitted_at":"2026-06-09T06:29:13Z","abstract_excerpt":"Transformer-based Speech Foundation Models excel in most Automatic Speech Recognition tasks but often suffer performance degradation when applied to domains with mismatched acoustic characteristics. While Parameter Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), adjust global attention, they lack the local context modeling crucial for capturing domain-specific variations. We propose GC-LoRA, a novel adapter architecture that injects Conformer-style local convolutional processing into pretrained Transformer encoders. By integrating a lightweight adapter to encoder atte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10464","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/2606.10464/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":"2026-06-10T01:10:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PT/l/yP3VLqRbJwbhXAYTSPdMcQI+tiFtSCEa/POLT090641i3wT+fTgfU7vmXzn2zNbmEwJIPBVDNNVp4UUBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T02:01:01.039823Z"},"content_sha256":"e291ff19571b1cab474b935867639a58910e14b68aaae74c2b51d2d304cb8902","schema_version":"1.0","event_id":"sha256:e291ff19571b1cab474b935867639a58910e14b68aaae74c2b51d2d304cb8902"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IMA4QIUMVIDUIRVIOJME56TA5K/bundle.json","state_url":"https://pith.science/pith/IMA4QIUMVIDUIRVIOJME56TA5K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IMA4QIUMVIDUIRVIOJME56TA5K/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":"2026-06-29T02:01:01Z","links":{"resolver":"https://pith.science/pith/IMA4QIUMVIDUIRVIOJME56TA5K","bundle":"https://pith.science/pith/IMA4QIUMVIDUIRVIOJME56TA5K/bundle.json","state":"https://pith.science/pith/IMA4QIUMVIDUIRVIOJME56TA5K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IMA4QIUMVIDUIRVIOJME56TA5K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IMA4QIUMVIDUIRVIOJME56TA5K","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":"dbc12b27c7160a16578ffa1449468d55054cb719e7bafb8169ab613831b992ea","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-06-09T06:29:13Z","title_canon_sha256":"69f7e2dc06be14f59ae993249823c3e94675f8583b1ffcc3e846ef9d974f8e6f"},"schema_version":"1.0","source":{"id":"2606.10464","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.10464","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"arxiv_version","alias_value":"2606.10464v1","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10464","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"pith_short_12","alias_value":"IMA4QIUMVIDU","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"pith_short_16","alias_value":"IMA4QIUMVIDUIRVI","created_at":"2026-06-10T01:10:20Z"},{"alias_kind":"pith_short_8","alias_value":"IMA4QIUM","created_at":"2026-06-10T01:10:20Z"}],"graph_snapshots":[{"event_id":"sha256:e291ff19571b1cab474b935867639a58910e14b68aaae74c2b51d2d304cb8902","target":"graph","created_at":"2026-06-10T01:10:20Z","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/2606.10464/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transformer-based Speech Foundation Models excel in most Automatic Speech Recognition tasks but often suffer performance degradation when applied to domains with mismatched acoustic characteristics. While Parameter Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), adjust global attention, they lack the local context modeling crucial for capturing domain-specific variations. We propose GC-LoRA, a novel adapter architecture that injects Conformer-style local convolutional processing into pretrained Transformer encoders. By integrating a lightweight adapter to encoder atte","authors_text":"Abeer Alwan, Kaiyuan Zhang, Mohan Shi, Natarajan Balaji Shankar, Zilai Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-06-09T06:29:13Z","title":"GC-LoRA: Gated Convolutional LoRA for Parameter-Efficient Acoustic Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10464","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":"sha256:1ea67296aaf6ce250bff4f6a09feaed5f10fcd5892fa0ad5380e9916b11a0440","target":"record","created_at":"2026-06-10T01:10:20Z","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":"dbc12b27c7160a16578ffa1449468d55054cb719e7bafb8169ab613831b992ea","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-06-09T06:29:13Z","title_canon_sha256":"69f7e2dc06be14f59ae993249823c3e94675f8583b1ffcc3e846ef9d974f8e6f"},"schema_version":"1.0","source":{"id":"2606.10464","kind":"arxiv","version":1}},"canonical_sha256":"4301c8228caa074446a872584efa60eaa88f59721560727ea5542e14b3699e44","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4301c8228caa074446a872584efa60eaa88f59721560727ea5542e14b3699e44","first_computed_at":"2026-06-10T01:10:20.574209Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T01:10:20.574209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PKT7zScAaQA8d7XByhymr0lzbsx+FxxO80954H0ECvgfC1usy3UsxW1oax/xuRqpsvFq92zGXmhlH0ax3cPxDQ==","signature_status":"signed_v1","signed_at":"2026-06-10T01:10:20.575001Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.10464","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ea67296aaf6ce250bff4f6a09feaed5f10fcd5892fa0ad5380e9916b11a0440","sha256:e291ff19571b1cab474b935867639a58910e14b68aaae74c2b51d2d304cb8902"],"state_sha256":"f28e4efbae9ee9f87ba72f27a9c02d6ec1cc2f6a4ff75a385309c9fda5e64f7a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vq3BZFuffmx37BDN3/c62YVEoQEvtRWZBZYDeR9NiUPEiCnYD+TxGYGpVDC+EL4Pii2/w2RXedfp/lUtAhsADA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T02:01:01.042195Z","bundle_sha256":"6199a6b7ee4f8c22d06e3036339e4fea4373c229e76a4e38fff9c3145472d727"}}