{"as_of":"2026-08-11T18:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:971777626fd0259096114f1fd0a5793e2f9b83fd0dbee6ab3297880759efb3d8","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T17:12:09.688401Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T17:15:32.992695Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T07:15:59.760491Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"cited_work":{"arxiv_id":"2501.12501","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.12501","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"446732f9-40f1-402c-a02b-207a3eebc2a4","year":2025},"citing_paper":{"arxiv_id":"2604.19797","last_updated":"2026-04-10T09:41:26Z","snapshot_observed_at":"2026-08-11T13:19:52.015842Z","submitted_at":"2026-04-10T09:41:26Z","title":"Enhancing ASR Performance in the Medical Domain for Dravidian Languages","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T17:15:32.992695Z"},"links":{"cited_paper":"/paper/2501.12501","citing_paper":"/paper/2604.19797"},"observation_digest":"sha256:3abce4092d41fb6b26cc7189f24e02a168682e444a9d8d868b6e4c13e3f66a92","observation_id":"54d15839-e2c7-4e5c-9bf1-4cb8645c9654","resolution":{"observed_at":"2026-05-11T07:15:59.770969Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.12501/citation-record","integrity":"/paper/2501.12501/integrity","json":"/paper/2501.12501/citation-record.json","paper":"/paper/2501.12501"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.586904Z","title":"Robust speech recognition via large-scale weak supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.586904Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:85ae9069acbb48821471b117d7a9beca4fd94a8e06a0f9daa48f063ef72db133","observation_id":"599554e6-500f-498e-b591-0cd13d527d5d","resolution":{"observed_at":"2026-08-10T17:12:09.586904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:10.017922Z","title":"Lora: Low-rank adaptation of large language models,","venue":null,"work_id":"6537227f-f03a-4af7-a77a-9af4ad3c4c7a","year":2022},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.592208Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:76652fbbe4e26e0f3f857bfde1af2badfeea02f40647b416727bc70ea469dbeb","observation_id":"8fe0b449-d69f-404f-8339-4e060e68e925","resolution":{"observed_at":"2026-08-10T17:12:10.024001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:10.000659Z","title":"An unsupervised deep domain adaptation approach for robust speech recognition,","venue":null,"work_id":"5106340b-7855-4504-b85f-132447ec9efb","year":2017},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.597131Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:7a8cf9da086477128d4fedfc0244994ac097b50c616236dd862a2ce75b218126","observation_id":"df23c583-ddae-43f8-82f0-71293a119dd4","resolution":{"observed_at":"2026-08-10T17:12:10.005635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.602169Z","title":"Unsupervised domain adaptation by backpropagation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.602169Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:42367eff3cf995fe4aad987b0befd3d0c3e88c1e2f5d26d967909966b52d73ba","observation_id":"fcd97461-b314-428e-bb68-c52db0b45e01","resolution":{"observed_at":"2026-08-10T17:12:09.602169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.971947Z","title":"Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmen- tation,","venue":null,"work_id":"fdb5b55a-1a84-48e5-bfcb-b35c7f1f541e","year":2017},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.608141Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:de6d6b1d704109848bf5885468546d8d8217374f4aca9497b719744a4f9c81b9","observation_id":"bcd9ed74-2d2e-429a-9941-46b921196731","resolution":{"observed_at":"2026-08-10T17:12:09.977178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.954749Z","title":"Domain adaptation via teacher- student learning for end-to-end speech recognition,","venue":null,"work_id":"389f8c1e-12be-488a-9521-eed0319dabd6","year":2019},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.612824Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:aa9cd68fd65711bd4a3221ec7c05e74f5e42358cd48b057ead7fde0073008aa4","observation_id":"35b5347d-de99-4a1e-a147-2660fafe1d76","resolution":{"observed_at":"2026-08-10T17:12:09.960137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.938448Z","title":"Domain adaptation of end-to- end speech recognition in low-resource settings,","venue":null,"work_id":"3bd64efe-a507-46f0-8963-aa8f46064913","year":2018},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.618473Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:15b51606805ab05f8a82b88579c548870e53a8069060fbe89efee84e864cb710","observation_id":"e3fd4c0b-64ac-48e2-8700-4a490f02caf8","resolution":{"observed_at":"2026-08-10T17:12:09.943550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.922832Z","title":"Domain adaptation using factorized hidden layer for robust automatic speech recognition","venue":null,"work_id":"0db3d142-0bb5-4ee5-803a-5fc88cc71cc7","year":2018},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.623221Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:b1f7bdd936d16ab9516b8f669b96886ecf0901f0e2f1e95e726802a2be479b59","observation_id":"d78f38cc-bb48-4bfc-9e6f-2759e8698718","resolution":{"observed_at":"2026-08-10T17:12:09.927903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.905707Z","title":"A comparison of parameter-efficient asr domain adaptation methods for universal speech and language models,","venue":null,"work_id":"3006d28d-ec23-48a9-a5a0-e245a9cf47e1","year":2024},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.628059Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:7795be0fe72e5c7c40bf02bf30387c662dcec4d27c7b406a316ce823e0482966","observation_id":"6a12a86f-c03a-4137-9b2a-71bf20270839","resolution":{"observed_at":"2026-08-10T17:12:09.911841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.633063Z","title":"Learning multiple visual do- mains with residual adapters,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.633063Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:0dd3eaa169a8d1b2268e3e899e3a20821eb92044e19a8104ce29bba7ba275758","observation_id":"b6c511c0-6dcb-4e0f-9415-e9cd3c4d533f","resolution":{"observed_at":"2026-08-10T17:12:09.633063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.873834Z","title":"Using synthetic audio to improve the recognition of out-of-vocabulary words in end-to-end asr systems,","venue":null,"work_id":"74a7d369-319f-4b22-9935-7eaa5caa35a8","year":2021},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.638683Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:8f830ce43cb1883e8f83598ef8de8103997c278c33d087d26911ad59bb139068","observation_id":"2efa35d7-e524-4089-91ae-55e4603047d4","resolution":{"observed_at":"2026-08-10T17:12:09.881087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16333","last_updated":"2023-05-22T18:45:20Z","snapshot_observed_at":"2026-08-01T03:30:55.328493Z","submitted_at":"2023-05-22T18:45:20Z","title":"Text Generation with Speech Synthesis for ASR Data Augmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16333","snapshot_observed_at":"2026-08-10T17:12:09.644429Z","title":"Text generation with speech synthesis for asr data augmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.644429Z"},"links":{"cited_paper":"/paper/2305.16333","citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:8753645985f3e92370f2302a959cead7c2d062ce4e80ea3b9fa7508549ee4864","observation_id":"a108cc30-fac5-4bbe-aa67-df0c9c7099a6","resolution":{"observed_at":"2026-08-10T17:12:09.644429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.02194","last_updated":"2021-06-11T23:10:43Z","snapshot_observed_at":"2026-08-10T23:50:26.821725Z","submitted_at":"2021-04-05T23:59:43Z","title":"Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.02194","snapshot_observed_at":"2026-08-10T17:12:09.651132Z","title":"Contextualized streaming end-to-end speech recognition with trie-based deep biasing and shallow fusion,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.651132Z"},"links":{"cited_paper":"/paper/2104.02194","citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:315c869e33dcf4852e9ba2f78ca9e49277a16ca60a2b2719fe22703fe97e63b6","observation_id":"ac914ec3-ac0a-4942-9560-7bbfc94212a0","resolution":{"observed_at":"2026-08-10T17:12:09.651132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.656618Z","title":"Llama 3 model card,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.656618Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:2998d0f450a7ee91c3bd197ec3d90a0e1aa7b9e948dbdacf65ad153292eae00c","observation_id":"31cedbf6-8316-4eb2-8cf5-eb17aa68fab7","resolution":{"observed_at":"2026-08-10T17:12:09.656618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05875","last_updated":"2024-04-08T21:15:36Z","snapshot_observed_at":"2026-08-07T12:08:06.802905Z","submitted_at":"2024-04-08T21:15:36Z","title":"CodecLM: Aligning Language Models with Tailored Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05875","snapshot_observed_at":"2026-08-10T17:12:09.661664Z","title":"Codeclm: Aligning language models with tailored synthetic data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.661664Z"},"links":{"cited_paper":"/paper/2404.05875","citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:dcf493b947c4e895e9c08dbeb30f13d740c775f1d96105b62adf7b03cc0c464e","observation_id":"b0e91473-4f47-4bc8-96ce-b0218d178772","resolution":{"observed_at":"2026-08-10T17:12:09.661664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.667897Z","title":"Librispeech: an asr corpus based on public domain audio books,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.667897Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:f345f6e4a0ac92af9b2c6e9c1b5282bf33f4b599b28e6087b979a39f2cf25173","observation_id":"c9ca3bec-4a11-4c42-a3c6-4a10a5d68682","resolution":{"observed_at":"2026-08-10T17:12:09.667897Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T17:12:09.672987Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.672987Z"},"links":{"citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:f0f55f4452158d8b4db69ee35af5a8de1bbb59579a75b89037fa8702458c8e43","observation_id":"44411f63-58af-4223-b4b3-d491c9c25841","resolution":{"observed_at":"2026-08-10T17:12:09.672987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.03285","last_updated":"2024-06-05T06:06:43Z","snapshot_observed_at":"2026-08-10T02:30:06.079083Z","submitted_at":"2023-11-06T17:26:17Z","title":"S-LoRA: Serving Thousands of Concurrent LoRA Adapters","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.03285","snapshot_observed_at":"2026-08-10T17:12:09.678075Z","title":"S-lora: Serving thousands of concurrent lora adapters,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.678075Z"},"links":{"cited_paper":"/paper/2311.03285","citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:1a6182e5f3c5623689e84068aaf769a6641020a0f82e18f8e4d820af69b5492a","observation_id":"b01641e0-3dc6-442f-86be-453017073497","resolution":{"observed_at":"2026-08-10T17:12:09.678075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.03732","last_updated":"2023-11-28T03:23:20Z","snapshot_observed_at":"2026-08-08T21:34:35.024914Z","submitted_at":"2023-11-28T03:23:20Z","title":"A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03732","snapshot_observed_at":"2026-08-10T17:12:09.683120Z","title":"A rank stabilization scaling factor for fine-tuning with lora,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.683120Z"},"links":{"cited_paper":"/paper/2312.03732","citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:a687ad18c2443204a84ef7d06b153ba471c6abfc013dc4b3388e74516081392e","observation_id":"7b13ba55-2bab-41b6-b294-fc2fe34732d5","resolution":{"observed_at":"2026-08-10T17:12:09.683120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02948","last_updated":"2025-04-09T06:54:20Z","snapshot_observed_at":"2026-08-09T04:47:57.368711Z","submitted_at":"2024-04-03T15:06:43Z","title":"PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02948","snapshot_observed_at":"2026-08-10T17:12:09.688401Z","title":"Pissa: Principal singular values and singular vectors adaptation of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T17:12:09.688401Z"},"links":{"cited_paper":"/paper/2404.02948","citing_paper":"/paper/2501.12501"},"observation_digest":"sha256:e64b005b7a1859cb9f690eaba66d687b30de5697618abbdbadaa68039bb6f8dd","observation_id":"ab50c507-eaf4-46d2-b91c-fab6c3e40cb9","resolution":{"observed_at":"2026-08-10T17:12:09.688401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.12501","last_updated":"2025-01-21T21:06:11Z","latest_version":1,"primary_category":"eess.AS","snapshot_observed_at":"2026-08-10T17:05:16.999335Z","submitted_at":"2025-01-21T21:06:11Z","title":"A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2501.12501."}