{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TR2X4UW7B7LJ56LA5AZ6F7DNKF","short_pith_number":"pith:TR2X4UW7","canonical_record":{"source":{"id":"2506.13596","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T15:23:07Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"828fe36912983203f7b5df8b6e97ea03c73748f8831defd086483f1d8cab4362","abstract_canon_sha256":"b8a44da477cd634c14ac235a16d397f665b910316092ebb11d0d366ccf528032"},"schema_version":"1.0"},"canonical_sha256":"9c757e52df0fd69ef960e833e2fc6d5164f311d5ccd4ee7724801bce77115e38","source":{"kind":"arxiv","id":"2506.13596","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13596","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13596v2","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13596","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"pith_short_12","alias_value":"TR2X4UW7B7LJ","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"TR2X4UW7B7LJ56LA","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"TR2X4UW7","created_at":"2026-07-05T11:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TR2X4UW7B7LJ56LA5AZ6F7DNKF","target":"record","payload":{"canonical_record":{"source":{"id":"2506.13596","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T15:23:07Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"828fe36912983203f7b5df8b6e97ea03c73748f8831defd086483f1d8cab4362","abstract_canon_sha256":"b8a44da477cd634c14ac235a16d397f665b910316092ebb11d0d366ccf528032"},"schema_version":"1.0"},"canonical_sha256":"9c757e52df0fd69ef960e833e2fc6d5164f311d5ccd4ee7724801bce77115e38","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:31.113604Z","signature_b64":"q/TqvfzcgP2v4A/Nv2Z8xnMcZql3bik9dPOxSNzSZWJYJPGuOQKZ8LsxrgWYzn8K6SYiqk4MO49M55ttdfqsBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c757e52df0fd69ef960e833e2fc6d5164f311d5ccd4ee7724801bce77115e38","last_reissued_at":"2026-07-05T11:32:31.113095Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:31.113095Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.13596","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:32:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XuksRU50P0CceQIFSgDfba3vn/K3qQR1Mk1us9y8q4oUSu/r6OiRuhrjKpyj3r/xT039b+4HbkomuskNwDJlAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:14:33.923355Z"},"content_sha256":"eb6cb3be795a1c69d2dd2de2320096b51de9aa62292a7745af272616328b54b2","schema_version":"1.0","event_id":"sha256:eb6cb3be795a1c69d2dd2de2320096b51de9aa62292a7745af272616328b54b2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TR2X4UW7B7LJ56LA5AZ6F7DNKF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Huy-Dat Tran, Long-Vu Hoang, Tuan Nguyen","submitted_at":"2025-06-16T15:23:07Z","abstract_excerpt":"This paper presents our system for the MLC-SLM Challenge 2025, focusing on multilingual speech recognition and language modeling with large language models (LLMs). Our approach combines a fine-tuned Whisper-large-v3 encoder with efficient projector architectures and various decoder configurations. We employ a three-stage training methodology that progressively optimizes the encoder, projector, and LLM components. Our system achieves competitive performance with a private test average WER/CER result of 16.63% using the Gemma3-12B and 18.6% using the Qwen2.5-7B as decoder-only language model."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13596","kind":"arxiv","version":2},"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/2506.13596/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":"2026-07-05T11:32:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qe9c3A6bXqoX5oznLZj1NAev18KPjvciJSVxeHJyImDI06oD5VQnnhHmXqFIb8KwWqXzNENWRgrjWi1W8QKdCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T10:14:33.923963Z"},"content_sha256":"777f29ba5efe5af616ea3ae3d127053ecbea946419c224347be373dc9b84d063","schema_version":"1.0","event_id":"sha256:777f29ba5efe5af616ea3ae3d127053ecbea946419c224347be373dc9b84d063"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TR2X4UW7B7LJ56LA5AZ6F7DNKF/bundle.json","state_url":"https://pith.science/pith/TR2X4UW7B7LJ56LA5AZ6F7DNKF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TR2X4UW7B7LJ56LA5AZ6F7DNKF/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":"2026-08-09T10:14:33Z","links":{"resolver":"https://pith.science/pith/TR2X4UW7B7LJ56LA5AZ6F7DNKF","bundle":"https://pith.science/pith/TR2X4UW7B7LJ56LA5AZ6F7DNKF/bundle.json","state":"https://pith.science/pith/TR2X4UW7B7LJ56LA5AZ6F7DNKF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TR2X4UW7B7LJ56LA5AZ6F7DNKF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TR2X4UW7B7LJ56LA5AZ6F7DNKF","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":"b8a44da477cd634c14ac235a16d397f665b910316092ebb11d0d366ccf528032","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T15:23:07Z","title_canon_sha256":"828fe36912983203f7b5df8b6e97ea03c73748f8831defd086483f1d8cab4362"},"schema_version":"1.0","source":{"id":"2506.13596","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13596","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13596v2","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13596","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"pith_short_12","alias_value":"TR2X4UW7B7LJ","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"TR2X4UW7B7LJ56LA","created_at":"2026-07-05T11:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"TR2X4UW7","created_at":"2026-07-05T11:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:777f29ba5efe5af616ea3ae3d127053ecbea946419c224347be373dc9b84d063","target":"graph","created_at":"2026-07-05T11:32:31Z","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/2506.13596/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents our system for the MLC-SLM Challenge 2025, focusing on multilingual speech recognition and language modeling with large language models (LLMs). Our approach combines a fine-tuned Whisper-large-v3 encoder with efficient projector architectures and various decoder configurations. We employ a three-stage training methodology that progressively optimizes the encoder, projector, and LLM components. Our system achieves competitive performance with a private test average WER/CER result of 16.63% using the Gemma3-12B and 18.6% using the Qwen2.5-7B as decoder-only language model.","authors_text":"Huy-Dat Tran, Long-Vu Hoang, Tuan Nguyen","cross_cats":["cs.SD","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T15:23:07Z","title":"Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13596","kind":"arxiv","version":2},"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:eb6cb3be795a1c69d2dd2de2320096b51de9aa62292a7745af272616328b54b2","target":"record","created_at":"2026-07-05T11:32:31Z","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":"b8a44da477cd634c14ac235a16d397f665b910316092ebb11d0d366ccf528032","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T15:23:07Z","title_canon_sha256":"828fe36912983203f7b5df8b6e97ea03c73748f8831defd086483f1d8cab4362"},"schema_version":"1.0","source":{"id":"2506.13596","kind":"arxiv","version":2}},"canonical_sha256":"9c757e52df0fd69ef960e833e2fc6d5164f311d5ccd4ee7724801bce77115e38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c757e52df0fd69ef960e833e2fc6d5164f311d5ccd4ee7724801bce77115e38","first_computed_at":"2026-07-05T11:32:31.113095Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:32:31.113095Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q/TqvfzcgP2v4A/Nv2Z8xnMcZql3bik9dPOxSNzSZWJYJPGuOQKZ8LsxrgWYzn8K6SYiqk4MO49M55ttdfqsBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:32:31.113604Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.13596","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb6cb3be795a1c69d2dd2de2320096b51de9aa62292a7745af272616328b54b2","sha256:777f29ba5efe5af616ea3ae3d127053ecbea946419c224347be373dc9b84d063"],"state_sha256":"de0bf8de87d03a0c70378c31f6678193c9851a76c84b69e6d2f41b2a15b37e45"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"53Y9JUMVTPSudMm2cvv5bnsTFbMVhARDPSbfLKJVBajfUse1lb82aHJ5E9p54jYn2xeEBzsiNSBGI+SMVXzkCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T10:14:33.929350Z","bundle_sha256":"8c41b2cc8e18262e4d24b158e9554f6b9d62a6289702594a3488ee22d4ec1792"}}