{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BKPCKBDID6VCALO2AMX3MSDCRR","short_pith_number":"pith:BKPCKBDI","canonical_record":{"source":{"id":"2506.12059","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-05-31T07:26:44Z","cross_cats_sorted":["cs.AI","cs.CL","cs.SD"],"title_canon_sha256":"e876bb7ee0a3dbb4cf8b24d9809e95f6b8731bc791f17d47ba5afd24f6e8fd4d","abstract_canon_sha256":"398790c9482bb72c3733d8a2c975ab268964ee6974dbcd3b508e32f8a23957d8"},"schema_version":"1.0"},"canonical_sha256":"0a9e2504681faa202dda032fb648628c6cc516b79d518efe11c980a45e491568","source":{"kind":"arxiv","id":"2506.12059","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12059","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12059v1","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12059","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"pith_short_12","alias_value":"BKPCKBDID6VC","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"pith_short_16","alias_value":"BKPCKBDID6VCALO2","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"pith_short_8","alias_value":"BKPCKBDI","created_at":"2026-07-05T11:21:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BKPCKBDID6VCALO2AMX3MSDCRR","target":"record","payload":{"canonical_record":{"source":{"id":"2506.12059","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-05-31T07:26:44Z","cross_cats_sorted":["cs.AI","cs.CL","cs.SD"],"title_canon_sha256":"e876bb7ee0a3dbb4cf8b24d9809e95f6b8731bc791f17d47ba5afd24f6e8fd4d","abstract_canon_sha256":"398790c9482bb72c3733d8a2c975ab268964ee6974dbcd3b508e32f8a23957d8"},"schema_version":"1.0"},"canonical_sha256":"0a9e2504681faa202dda032fb648628c6cc516b79d518efe11c980a45e491568","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:16.454660Z","signature_b64":"2RxQzUdrL2zS4xJMePcib10TG/gOZ0FIrvRqApE/V2e4LU3+5dxX1xNtqPpDi1h/B5pyJp8nYv0cD70rCt5aAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a9e2504681faa202dda032fb648628c6cc516b79d518efe11c980a45e491568","last_reissued_at":"2026-07-05T11:21:16.454237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:16.454237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.12059","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":"2026-07-05T11:21:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zy/YlWC9osDPNbd16/djDI7nQbJA0bnl/poL5DwaA0C1bVtClbmwG6PZNmnPS/NFWCEZp4AQ34LE7zhJALSLCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:26:14.564692Z"},"content_sha256":"06cbd365db92c505b21c046389b1c0b12a930bce5a3605f08d4c840eec5b461e","schema_version":"1.0","event_id":"sha256:06cbd365db92c505b21c046389b1c0b12a930bce5a3605f08d4c840eec5b461e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BKPCKBDID6VCALO2AMX3MSDCRR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CMT-LLM: Contextual Multi-Talker ASR Utilizing Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Jiajun He, Koichi Miyazaki, Naoki Sawada, Tomoki Toda","submitted_at":"2025-05-31T07:26:44Z","abstract_excerpt":"In real-world applications, automatic speech recognition (ASR) systems must handle overlapping speech from multiple speakers and recognize rare words like technical terms. Traditional methods address multi-talker ASR and contextual biasing separately, limiting performance in complex scenarios. We propose a unified framework that combines multi-talker overlapping speech recognition and contextual biasing into a single task. Our ASR method integrates pretrained speech encoders and large language models (LLMs), using optimized finetuning strategies. We also introduce a two-stage filtering algorit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12059","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/2506.12059/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":"2026-07-05T11:21:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HpC0IpZkjHPVRIMljrW9MIGdU8eFRQ+INcVamOX+h+jLx+/ux+e/v9sVoYbsff1n0r3eJZPmIzQq8CmM6kGQDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T07:26:14.565238Z"},"content_sha256":"90c44647295129dcfeeeb60ca4e6cb99c6eace12c46f8f908d1df4aca77183af","schema_version":"1.0","event_id":"sha256:90c44647295129dcfeeeb60ca4e6cb99c6eace12c46f8f908d1df4aca77183af"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BKPCKBDID6VCALO2AMX3MSDCRR/bundle.json","state_url":"https://pith.science/pith/BKPCKBDID6VCALO2AMX3MSDCRR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BKPCKBDID6VCALO2AMX3MSDCRR/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":"2026-08-08T07:26:14Z","links":{"resolver":"https://pith.science/pith/BKPCKBDID6VCALO2AMX3MSDCRR","bundle":"https://pith.science/pith/BKPCKBDID6VCALO2AMX3MSDCRR/bundle.json","state":"https://pith.science/pith/BKPCKBDID6VCALO2AMX3MSDCRR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BKPCKBDID6VCALO2AMX3MSDCRR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BKPCKBDID6VCALO2AMX3MSDCRR","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":"398790c9482bb72c3733d8a2c975ab268964ee6974dbcd3b508e32f8a23957d8","cross_cats_sorted":["cs.AI","cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-05-31T07:26:44Z","title_canon_sha256":"e876bb7ee0a3dbb4cf8b24d9809e95f6b8731bc791f17d47ba5afd24f6e8fd4d"},"schema_version":"1.0","source":{"id":"2506.12059","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.12059","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"arxiv_version","alias_value":"2506.12059v1","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12059","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"pith_short_12","alias_value":"BKPCKBDID6VC","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"pith_short_16","alias_value":"BKPCKBDID6VCALO2","created_at":"2026-07-05T11:21:16Z"},{"alias_kind":"pith_short_8","alias_value":"BKPCKBDI","created_at":"2026-07-05T11:21:16Z"}],"graph_snapshots":[{"event_id":"sha256:90c44647295129dcfeeeb60ca4e6cb99c6eace12c46f8f908d1df4aca77183af","target":"graph","created_at":"2026-07-05T11:21:16Z","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/2506.12059/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In real-world applications, automatic speech recognition (ASR) systems must handle overlapping speech from multiple speakers and recognize rare words like technical terms. Traditional methods address multi-talker ASR and contextual biasing separately, limiting performance in complex scenarios. We propose a unified framework that combines multi-talker overlapping speech recognition and contextual biasing into a single task. Our ASR method integrates pretrained speech encoders and large language models (LLMs), using optimized finetuning strategies. We also introduce a two-stage filtering algorit","authors_text":"Jiajun He, Koichi Miyazaki, Naoki Sawada, Tomoki Toda","cross_cats":["cs.AI","cs.CL","cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-05-31T07:26:44Z","title":"CMT-LLM: Contextual Multi-Talker ASR Utilizing Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12059","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":"sha256:06cbd365db92c505b21c046389b1c0b12a930bce5a3605f08d4c840eec5b461e","target":"record","created_at":"2026-07-05T11:21:16Z","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":"398790c9482bb72c3733d8a2c975ab268964ee6974dbcd3b508e32f8a23957d8","cross_cats_sorted":["cs.AI","cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2025-05-31T07:26:44Z","title_canon_sha256":"e876bb7ee0a3dbb4cf8b24d9809e95f6b8731bc791f17d47ba5afd24f6e8fd4d"},"schema_version":"1.0","source":{"id":"2506.12059","kind":"arxiv","version":1}},"canonical_sha256":"0a9e2504681faa202dda032fb648628c6cc516b79d518efe11c980a45e491568","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0a9e2504681faa202dda032fb648628c6cc516b79d518efe11c980a45e491568","first_computed_at":"2026-07-05T11:21:16.454237Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:16.454237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2RxQzUdrL2zS4xJMePcib10TG/gOZ0FIrvRqApE/V2e4LU3+5dxX1xNtqPpDi1h/B5pyJp8nYv0cD70rCt5aAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:16.454660Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.12059","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:06cbd365db92c505b21c046389b1c0b12a930bce5a3605f08d4c840eec5b461e","sha256:90c44647295129dcfeeeb60ca4e6cb99c6eace12c46f8f908d1df4aca77183af"],"state_sha256":"bc062c9353eb6c8798ca318f88bcaca7c0832ed17221477f1132b84424bcc709"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9sXCWMvULeA3EPO65S1uyF72W9QnKRaGhipTgdoVQVF5qJS1k1dvl4yDWHunr4GkXeXLcmnOO5aPOT1p076WAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T07:26:14.569600Z","bundle_sha256":"77eb9ee73e36a67b6d620060e861035dc1a78d5c256388bc07035fb1c5fe6431"}}