{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:GUIZUHKUCCVTINWQRG6CN5EPXH","short_pith_number":"pith:GUIZUHKU","canonical_record":{"source":{"id":"2605.19711","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-19T11:48:32Z","cross_cats_sorted":[],"title_canon_sha256":"42d0b3e05adf4314c7c41e43f67f157fcb481fb2825e8f86185c7bece2ba893d","abstract_canon_sha256":"6d87e0bd189d9a97caeaa66143d16c8b8079d92d34bd68713bc5e11d5c71774b"},"schema_version":"1.0"},"canonical_sha256":"35119a1d5410ab3436d089bc26f48fb9ca98d103eef706b3dd3afd7273669217","source":{"kind":"arxiv","id":"2605.19711","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19711","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19711v1","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19711","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"pith_short_12","alias_value":"GUIZUHKUCCVT","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"pith_short_16","alias_value":"GUIZUHKUCCVTINWQ","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"pith_short_8","alias_value":"GUIZUHKU","created_at":"2026-05-20T01:05:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:GUIZUHKUCCVTINWQRG6CN5EPXH","target":"record","payload":{"canonical_record":{"source":{"id":"2605.19711","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-19T11:48:32Z","cross_cats_sorted":[],"title_canon_sha256":"42d0b3e05adf4314c7c41e43f67f157fcb481fb2825e8f86185c7bece2ba893d","abstract_canon_sha256":"6d87e0bd189d9a97caeaa66143d16c8b8079d92d34bd68713bc5e11d5c71774b"},"schema_version":"1.0"},"canonical_sha256":"35119a1d5410ab3436d089bc26f48fb9ca98d103eef706b3dd3afd7273669217","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:05:58.725925Z","signature_b64":"dDfAYH9cREJ5nKF5jWfVPBH00OJrlLQNI91Lvu2/WgQxM88EexWyw02N9Qmn4RLN5NT3Pyusl2J+ljNewds4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35119a1d5410ab3436d089bc26f48fb9ca98d103eef706b3dd3afd7273669217","last_reissued_at":"2026-05-20T01:05:58.725430Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:05:58.725430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.19711","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":"2026-05-20T01:05:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H5lwiC+V97vaHFqLAjBqY+8zRwLsnuhQ0e5lH70wmgk9XGrwzh2eg1n1ukePbdcJuJ8ieCgJoh1RuvAuxqaEDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T19:59:52.009108Z"},"content_sha256":"cce90c9459791e54c8eba0443fd703bae36b53a2e821f8f3be0200adb8c05f2e","schema_version":"1.0","event_id":"sha256:cce90c9459791e54c8eba0443fd703bae36b53a2e821f8f3be0200adb8c05f2e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:GUIZUHKUCCVTINWQRG6CN5EPXH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Martijn Wieling, Reihaneh Amooie, Rik van Noord, Wietse de Vries, Yun Hao","submitted_at":"2026-05-19T11:48:32Z","abstract_excerpt":"Automatic speech recognition (ASR) has improved substantially in recent years, yet performance remains limited for low-resource languages. Large language models (LLMs) have shown promise for improving ASR through generative error correction (GER), but their effectiveness in low-resource settings remains underexplored. In addition, it remains unclear to what extent data contamination influences the reported improvements in LLM-based GER. This study investigates LLM-based GER for low-resource Frisian. In addition to a public corpus, we construct and use a Frisian offline dataset with non-public "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19711","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/2605.19711/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":"2026-05-20T01:05:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q0XmzpcmeCp4QM8TNA00G3lb+q+bZ4cLXawiGq6/xWJUqQZIj9jkfVZmnk/tHkWMXk64CTznpZbjO22eMOU2CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T19:59:52.009672Z"},"content_sha256":"81130db6136016c593659b91e2ab764351d4f5c2e50ba9ec382bb5ee32989cc9","schema_version":"1.0","event_id":"sha256:81130db6136016c593659b91e2ab764351d4f5c2e50ba9ec382bb5ee32989cc9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GUIZUHKUCCVTINWQRG6CN5EPXH/bundle.json","state_url":"https://pith.science/pith/GUIZUHKUCCVTINWQRG6CN5EPXH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GUIZUHKUCCVTINWQRG6CN5EPXH/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":"2026-05-23T19:59:52Z","links":{"resolver":"https://pith.science/pith/GUIZUHKUCCVTINWQRG6CN5EPXH","bundle":"https://pith.science/pith/GUIZUHKUCCVTINWQRG6CN5EPXH/bundle.json","state":"https://pith.science/pith/GUIZUHKUCCVTINWQRG6CN5EPXH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GUIZUHKUCCVTINWQRG6CN5EPXH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GUIZUHKUCCVTINWQRG6CN5EPXH","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":"6d87e0bd189d9a97caeaa66143d16c8b8079d92d34bd68713bc5e11d5c71774b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-19T11:48:32Z","title_canon_sha256":"42d0b3e05adf4314c7c41e43f67f157fcb481fb2825e8f86185c7bece2ba893d"},"schema_version":"1.0","source":{"id":"2605.19711","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19711","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19711v1","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19711","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"pith_short_12","alias_value":"GUIZUHKUCCVT","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"pith_short_16","alias_value":"GUIZUHKUCCVTINWQ","created_at":"2026-05-20T01:05:58Z"},{"alias_kind":"pith_short_8","alias_value":"GUIZUHKU","created_at":"2026-05-20T01:05:58Z"}],"graph_snapshots":[{"event_id":"sha256:81130db6136016c593659b91e2ab764351d4f5c2e50ba9ec382bb5ee32989cc9","target":"graph","created_at":"2026-05-20T01:05:58Z","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/2605.19711/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatic speech recognition (ASR) has improved substantially in recent years, yet performance remains limited for low-resource languages. Large language models (LLMs) have shown promise for improving ASR through generative error correction (GER), but their effectiveness in low-resource settings remains underexplored. In addition, it remains unclear to what extent data contamination influences the reported improvements in LLM-based GER. This study investigates LLM-based GER for low-resource Frisian. In addition to a public corpus, we construct and use a Frisian offline dataset with non-public ","authors_text":"Martijn Wieling, Reihaneh Amooie, Rik van Noord, Wietse de Vries, Yun Hao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-19T11:48:32Z","title":"Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19711","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:cce90c9459791e54c8eba0443fd703bae36b53a2e821f8f3be0200adb8c05f2e","target":"record","created_at":"2026-05-20T01:05:58Z","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":"6d87e0bd189d9a97caeaa66143d16c8b8079d92d34bd68713bc5e11d5c71774b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-19T11:48:32Z","title_canon_sha256":"42d0b3e05adf4314c7c41e43f67f157fcb481fb2825e8f86185c7bece2ba893d"},"schema_version":"1.0","source":{"id":"2605.19711","kind":"arxiv","version":1}},"canonical_sha256":"35119a1d5410ab3436d089bc26f48fb9ca98d103eef706b3dd3afd7273669217","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35119a1d5410ab3436d089bc26f48fb9ca98d103eef706b3dd3afd7273669217","first_computed_at":"2026-05-20T01:05:58.725430Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:05:58.725430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dDfAYH9cREJ5nKF5jWfVPBH00OJrlLQNI91Lvu2/WgQxM88EexWyw02N9Qmn4RLN5NT3Pyusl2J+ljNewds4Aw==","signature_status":"signed_v1","signed_at":"2026-05-20T01:05:58.725925Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.19711","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cce90c9459791e54c8eba0443fd703bae36b53a2e821f8f3be0200adb8c05f2e","sha256:81130db6136016c593659b91e2ab764351d4f5c2e50ba9ec382bb5ee32989cc9"],"state_sha256":"c1348353824409f1241c9b7effc76721beafea0f720f1d6becad37ceb1b53d29"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xAeZSd7iObl0i7DO5LTmQbbI2ct0emiuyZE7sJ/IfBeHr77qYvGwe6oH2jZzXhNwQBmsM/036oaSHVL/DytUCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T19:59:52.012657Z","bundle_sha256":"7537e048cb51f8c8407cfdbaa3b3e88e9b43e84e28e3dcac106abbb30a6c6494"}}