{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DYVLQXL26UA32UJ6AELJUVFSU7","short_pith_number":"pith:DYVLQXL2","canonical_record":{"source":{"id":"2502.17380","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-02-24T18:06:57Z","cross_cats_sorted":["cs.AI","cs.CL","eess.AS"],"title_canon_sha256":"03e504750a18f4414825901055570aa4344cd1371ea6421070f68667de94d8f0","abstract_canon_sha256":"59c90738f654096c2f495c4bd7efa3c661e9d633cf90090d4a1eae7a7411a347"},"schema_version":"1.0"},"canonical_sha256":"1e2ab85d7af501bd513e01169a54b2a7e5e3412e9a2c6f2dfd3194a038eca3d0","source":{"kind":"arxiv","id":"2502.17380","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.17380","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"arxiv_version","alias_value":"2502.17380v3","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17380","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"pith_short_12","alias_value":"DYVLQXL26UA3","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"pith_short_16","alias_value":"DYVLQXL26UA32UJ6","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"pith_short_8","alias_value":"DYVLQXL2","created_at":"2026-07-05T11:33:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DYVLQXL26UA32UJ6AELJUVFSU7","target":"record","payload":{"canonical_record":{"source":{"id":"2502.17380","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-02-24T18:06:57Z","cross_cats_sorted":["cs.AI","cs.CL","eess.AS"],"title_canon_sha256":"03e504750a18f4414825901055570aa4344cd1371ea6421070f68667de94d8f0","abstract_canon_sha256":"59c90738f654096c2f495c4bd7efa3c661e9d633cf90090d4a1eae7a7411a347"},"schema_version":"1.0"},"canonical_sha256":"1e2ab85d7af501bd513e01169a54b2a7e5e3412e9a2c6f2dfd3194a038eca3d0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:33:20.547901Z","signature_b64":"EGqVsC9spaW3F0Il8FnR08+2k63h+vJn+yAt50TtI4OrswF9wUbD8q8SFUzOI8CiEo/WdQSeE5gAJoOCFMDqDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e2ab85d7af501bd513e01169a54b2a7e5e3412e9a2c6f2dfd3194a038eca3d0","last_reissued_at":"2026-07-05T11:33:20.547433Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:33:20.547433Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.17380","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":"2026-07-05T11:33:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oyVvO5Qt5IgaqPO6M6QbgC/Ca9qkFXwt+cWFO1mscLD+ApVmU28SQ92DtaCPf84K61WpaCyXEFXKhZpe6hfJDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:59:09.854926Z"},"content_sha256":"51b92895a270e779bc56aecd7d5ed29db3b2a5968d8ce58c3eac8f15c9a5a436","schema_version":"1.0","event_id":"sha256:51b92895a270e779bc56aecd7d5ed29db3b2a5968d8ce58c3eac8f15c9a5a436"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DYVLQXL26UA32UJ6AELJUVFSU7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Chao Zhang, Guangzhi Sun, Qiuming Zhao","submitted_at":"2025-02-24T18:06:57Z","abstract_excerpt":"Language diversity presents a significant challenge in speech-to-text (S2T) tasks, such as automatic speech recognition and translation. Traditional multi-lingual multi-task training approaches aim to address this by jointly optimising multiple speech recognition and translation tasks across various languages. While models like Whisper, built on these strategies, demonstrate strong performance, they still face issues of high computational cost, language interference, suboptimal training configurations, and limited extensibility. To overcome these challenges, we introduce LoRS-Merging (low-rank"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17380","kind":"arxiv","version":3},"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/2502.17380/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":"2026-07-05T11:33:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xqeNHbyVDcCA7vl2B7B21qnF97lFHOa957+r1/W9Ylx8F6Bie3WjzHN52td2C3c+01/BEuVGxrkACYcd1aTyBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T05:59:09.855360Z"},"content_sha256":"b10f2d090db8168edbacb2ea551ab3f035a9d91ec976cfaadeeeb12dedd1d8f8","schema_version":"1.0","event_id":"sha256:b10f2d090db8168edbacb2ea551ab3f035a9d91ec976cfaadeeeb12dedd1d8f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DYVLQXL26UA32UJ6AELJUVFSU7/bundle.json","state_url":"https://pith.science/pith/DYVLQXL26UA32UJ6AELJUVFSU7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DYVLQXL26UA32UJ6AELJUVFSU7/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":"2026-08-06T05:59:09Z","links":{"resolver":"https://pith.science/pith/DYVLQXL26UA32UJ6AELJUVFSU7","bundle":"https://pith.science/pith/DYVLQXL26UA32UJ6AELJUVFSU7/bundle.json","state":"https://pith.science/pith/DYVLQXL26UA32UJ6AELJUVFSU7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DYVLQXL26UA32UJ6AELJUVFSU7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DYVLQXL26UA32UJ6AELJUVFSU7","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":"59c90738f654096c2f495c4bd7efa3c661e9d633cf90090d4a1eae7a7411a347","cross_cats_sorted":["cs.AI","cs.CL","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-02-24T18:06:57Z","title_canon_sha256":"03e504750a18f4414825901055570aa4344cd1371ea6421070f68667de94d8f0"},"schema_version":"1.0","source":{"id":"2502.17380","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.17380","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"arxiv_version","alias_value":"2502.17380v3","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17380","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"pith_short_12","alias_value":"DYVLQXL26UA3","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"pith_short_16","alias_value":"DYVLQXL26UA32UJ6","created_at":"2026-07-05T11:33:20Z"},{"alias_kind":"pith_short_8","alias_value":"DYVLQXL2","created_at":"2026-07-05T11:33:20Z"}],"graph_snapshots":[{"event_id":"sha256:b10f2d090db8168edbacb2ea551ab3f035a9d91ec976cfaadeeeb12dedd1d8f8","target":"graph","created_at":"2026-07-05T11:33:20Z","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/2502.17380/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Language diversity presents a significant challenge in speech-to-text (S2T) tasks, such as automatic speech recognition and translation. Traditional multi-lingual multi-task training approaches aim to address this by jointly optimising multiple speech recognition and translation tasks across various languages. While models like Whisper, built on these strategies, demonstrate strong performance, they still face issues of high computational cost, language interference, suboptimal training configurations, and limited extensibility. To overcome these challenges, we introduce LoRS-Merging (low-rank","authors_text":"Chao Zhang, Guangzhi Sun, Qiuming Zhao","cross_cats":["cs.AI","cs.CL","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-02-24T18:06:57Z","title":"Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17380","kind":"arxiv","version":3},"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":"sha256:51b92895a270e779bc56aecd7d5ed29db3b2a5968d8ce58c3eac8f15c9a5a436","target":"record","created_at":"2026-07-05T11:33:20Z","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":"59c90738f654096c2f495c4bd7efa3c661e9d633cf90090d4a1eae7a7411a347","cross_cats_sorted":["cs.AI","cs.CL","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-02-24T18:06:57Z","title_canon_sha256":"03e504750a18f4414825901055570aa4344cd1371ea6421070f68667de94d8f0"},"schema_version":"1.0","source":{"id":"2502.17380","kind":"arxiv","version":3}},"canonical_sha256":"1e2ab85d7af501bd513e01169a54b2a7e5e3412e9a2c6f2dfd3194a038eca3d0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1e2ab85d7af501bd513e01169a54b2a7e5e3412e9a2c6f2dfd3194a038eca3d0","first_computed_at":"2026-07-05T11:33:20.547433Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:20.547433Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EGqVsC9spaW3F0Il8FnR08+2k63h+vJn+yAt50TtI4OrswF9wUbD8q8SFUzOI8CiEo/WdQSeE5gAJoOCFMDqDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:20.547901Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.17380","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51b92895a270e779bc56aecd7d5ed29db3b2a5968d8ce58c3eac8f15c9a5a436","sha256:b10f2d090db8168edbacb2ea551ab3f035a9d91ec976cfaadeeeb12dedd1d8f8"],"state_sha256":"2f6ee3eeb0c3d1ece84dcc596a92be45fcb9138390bd184e3040a6859f25700f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AMxQdoCWs1TxR5mLCN3n722XXCxwQEW9DXmIqIbR/P3l9/GpYu+j1r80ujsOPD/BBsuTxL9quy9uTNwXNyZ0BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T05:59:09.859576Z","bundle_sha256":"a02f8ce0f053c2475a2125d531f0d05dd4d5bfda9fbc2a674069170d925e519b"}}