{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:PBRP37DBEFZAGGGMEGPW336ZOZ","short_pith_number":"pith:PBRP37DB","canonical_record":{"source":{"id":"2408.09491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-08-18T14:10:35Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"d371837806aae971c5e0135a0d82fed6385b5aa34fd82ed6aeb665328bbb7cb9","abstract_canon_sha256":"1e09e03f797fb1eb14f882d9bc46d7c78cd1bfc69b28ad316d696825cd8841a3"},"schema_version":"1.0"},"canonical_sha256":"7862fdfc6121720318cc219f6defd97674bf806815f27deff5934baa85613f78","source":{"kind":"arxiv","id":"2408.09491","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.09491","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"arxiv_version","alias_value":"2408.09491v1","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.09491","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"pith_short_12","alias_value":"PBRP37DBEFZA","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"pith_short_16","alias_value":"PBRP37DBEFZAGGGM","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"pith_short_8","alias_value":"PBRP37DB","created_at":"2026-07-05T08:56:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:PBRP37DBEFZAGGGMEGPW336ZOZ","target":"record","payload":{"canonical_record":{"source":{"id":"2408.09491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-08-18T14:10:35Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"d371837806aae971c5e0135a0d82fed6385b5aa34fd82ed6aeb665328bbb7cb9","abstract_canon_sha256":"1e09e03f797fb1eb14f882d9bc46d7c78cd1bfc69b28ad316d696825cd8841a3"},"schema_version":"1.0"},"canonical_sha256":"7862fdfc6121720318cc219f6defd97674bf806815f27deff5934baa85613f78","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:56:33.311112Z","signature_b64":"DizVPMDZyY7tnDBHsOB1mmJM1+xE47XJj6SbEp9e4FR+VPbBWuvhoMYjmpQDyeFymTvflWQaPECGIgDz7ob4Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7862fdfc6121720318cc219f6defd97674bf806815f27deff5934baa85613f78","last_reissued_at":"2026-07-05T08:56:33.310628Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:56:33.310628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.09491","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":"2026-07-05T08:56:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5sKqecoTdZEESIF5ICiK34Uy0ooYXyvI7YZlzamrTIf3hSFwGwTUCnC6RktOAy8r1yMuAOmEjJRr1Sg18G+aAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:51:06.281547Z"},"content_sha256":"40c11a6da82175aa96ba6ae0025392b5ed144980ef9e6eb4c761b7ef7b25c485","schema_version":"1.0","event_id":"sha256:40c11a6da82175aa96ba6ae0025392b5ed144980ef9e6eb4c761b7ef7b25c485"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:PBRP37DBEFZAGGGMEGPW336ZOZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Transcription Prompt-based Efficient Audio Large Language Model for Robust Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Lei Xie, Long Ma, Songjun Cao, Xiong Wang, Yangze Li, Yike Zhang","submitted_at":"2024-08-18T14:10:35Z","abstract_excerpt":"Audio-LLM introduces audio modality into a large language model (LLM) to enable a powerful LLM to recognize, understand, and generate audio. However, during speech recognition in noisy environments, we observed the presence of illusions and repetition issues in audio-LLM, leading to substitution and insertion errors. This paper proposes a transcription prompt-based audio-LLM by introducing an ASR expert as a transcription tokenizer and a hybrid Autoregressive (AR) Non-autoregressive (NAR) decoding approach to solve the above problems. Experiments on 10k-hour WenetSpeech Mandarin corpus show th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.09491","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/2408.09491/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":"2026-07-05T08:56:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"btFdqOVc/kGNEwLcg75Xxf6FUYt05eR8ygfWRq9CwkPY6iej/BuDCbLGF0DdYkq1Dy1/XRwcoU9out8nKrXDAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:51:06.282097Z"},"content_sha256":"360276c04bea18681a4378b2d43bcb031935fa43d08a27a5744766857a8cff1f","schema_version":"1.0","event_id":"sha256:360276c04bea18681a4378b2d43bcb031935fa43d08a27a5744766857a8cff1f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PBRP37DBEFZAGGGMEGPW336ZOZ/bundle.json","state_url":"https://pith.science/pith/PBRP37DBEFZAGGGMEGPW336ZOZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PBRP37DBEFZAGGGMEGPW336ZOZ/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":"2026-08-09T08:51:06Z","links":{"resolver":"https://pith.science/pith/PBRP37DBEFZAGGGMEGPW336ZOZ","bundle":"https://pith.science/pith/PBRP37DBEFZAGGGMEGPW336ZOZ/bundle.json","state":"https://pith.science/pith/PBRP37DBEFZAGGGMEGPW336ZOZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PBRP37DBEFZAGGGMEGPW336ZOZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:PBRP37DBEFZAGGGMEGPW336ZOZ","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":"1e09e03f797fb1eb14f882d9bc46d7c78cd1bfc69b28ad316d696825cd8841a3","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-08-18T14:10:35Z","title_canon_sha256":"d371837806aae971c5e0135a0d82fed6385b5aa34fd82ed6aeb665328bbb7cb9"},"schema_version":"1.0","source":{"id":"2408.09491","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.09491","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"arxiv_version","alias_value":"2408.09491v1","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.09491","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"pith_short_12","alias_value":"PBRP37DBEFZA","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"pith_short_16","alias_value":"PBRP37DBEFZAGGGM","created_at":"2026-07-05T08:56:33Z"},{"alias_kind":"pith_short_8","alias_value":"PBRP37DB","created_at":"2026-07-05T08:56:33Z"}],"graph_snapshots":[{"event_id":"sha256:360276c04bea18681a4378b2d43bcb031935fa43d08a27a5744766857a8cff1f","target":"graph","created_at":"2026-07-05T08:56:33Z","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/2408.09491/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Audio-LLM introduces audio modality into a large language model (LLM) to enable a powerful LLM to recognize, understand, and generate audio. However, during speech recognition in noisy environments, we observed the presence of illusions and repetition issues in audio-LLM, leading to substitution and insertion errors. This paper proposes a transcription prompt-based audio-LLM by introducing an ASR expert as a transcription tokenizer and a hybrid Autoregressive (AR) Non-autoregressive (NAR) decoding approach to solve the above problems. Experiments on 10k-hour WenetSpeech Mandarin corpus show th","authors_text":"Lei Xie, Long Ma, Songjun Cao, Xiong Wang, Yangze Li, Yike Zhang","cross_cats":["eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-08-18T14:10:35Z","title":"A Transcription Prompt-based Efficient Audio Large Language Model for Robust Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.09491","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":"sha256:40c11a6da82175aa96ba6ae0025392b5ed144980ef9e6eb4c761b7ef7b25c485","target":"record","created_at":"2026-07-05T08:56:33Z","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":"1e09e03f797fb1eb14f882d9bc46d7c78cd1bfc69b28ad316d696825cd8841a3","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-08-18T14:10:35Z","title_canon_sha256":"d371837806aae971c5e0135a0d82fed6385b5aa34fd82ed6aeb665328bbb7cb9"},"schema_version":"1.0","source":{"id":"2408.09491","kind":"arxiv","version":1}},"canonical_sha256":"7862fdfc6121720318cc219f6defd97674bf806815f27deff5934baa85613f78","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7862fdfc6121720318cc219f6defd97674bf806815f27deff5934baa85613f78","first_computed_at":"2026-07-05T08:56:33.310628Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:56:33.310628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DizVPMDZyY7tnDBHsOB1mmJM1+xE47XJj6SbEp9e4FR+VPbBWuvhoMYjmpQDyeFymTvflWQaPECGIgDz7ob4Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T08:56:33.311112Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.09491","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:40c11a6da82175aa96ba6ae0025392b5ed144980ef9e6eb4c761b7ef7b25c485","sha256:360276c04bea18681a4378b2d43bcb031935fa43d08a27a5744766857a8cff1f"],"state_sha256":"d937874fa2309af179df86fb5135c18a0e01c106d1e5594a6e652ccc2495d748"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XzPEv786BsunjjabXznTD9fUb7HaZ52CcmhO6Jb+1V0zHovzS4WOBvkqASEHGv3EU6VVc1mAH00Ay6ghEQnSBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T08:51:06.286254Z","bundle_sha256":"5c75833365f83a520b68bc685b3531eb947e2cccf8ca24700ee2de43470ebce4"}}