{"as_of":"2026-08-08T15:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cdeac88f0e3fd700221ffd6a58816e135ff028ba6462224ecb3a1a45ce31246f","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:35:39.165139Z","state":"measured"},{"denominator":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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":"2026-08-07T12:35:37.174215Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T12:35:39.281526Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"cited_work":{"arxiv_id":"2505.24229","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.24229","snapshot_observed_at":"2026-08-07T12:35:39.281526Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","venue":"cs.CL","work_id":"c3095621-9cb4-4834-8096-83c1f02abe9b","year":2025},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.174215Z"},"links":{"cited_paper":"/paper/2505.24229","citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:d79a844af259a1127ca14d59b04b4cffc0795e3c2df0d249c0b5f5ba0b993f31","observation_id":"30cff8d2-8e53-433a-acc6-f2622ca1ebea","resolution":{"observed_at":"2026-08-07T12:35:39.360996Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.24229/citation-record","integrity":"/paper/2505.24229/integrity","json":"/paper/2505.24229/citation-record.json","paper":"/paper/2505.24229"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:44.180675Z","title":"three point five dollars","venue":null,"work_id":"19a1cd03-4df8-463e-958f-4f2c91a2735c","year":null},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:36.952665Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:4c2b0d0da58bf9867633b043f9c00ee70890a2c88bdfa6a0f26391d68a308896","observation_id":"64a3c503-f399-4368-904c-68be598bca5c","resolution":{"observed_at":"2026-08-07T12:35:44.270211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:43.947664Z","title":"By modifying the architecture of the pretrained model, we enable real-time processing while taking advantage of the pretrained weights","venue":null,"work_id":"799b1a2a-48dd-41ef-8142-2e535b7bd8b6","year":null},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.055548Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:1f590751be56425fdb5acd1293b1170f0f24d232def0668104f17f38f9eca388","observation_id":"cd9905f9-ca34-45d0-9335-a51c03166878","resolution":{"observed_at":"2026-08-07T12:35:44.078777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"cited_work":{"arxiv_id":"2505.24229","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.24229","snapshot_observed_at":"2026-08-07T12:35:39.281526Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","venue":"cs.CL","work_id":"c3095621-9cb4-4834-8096-83c1f02abe9b","year":2025},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.174215Z"},"links":{"cited_paper":"/paper/2505.24229","citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:d79a844af259a1127ca14d59b04b4cffc0795e3c2df0d249c0b5f5ba0b993f31","observation_id":"30cff8d2-8e53-433a-acc6-f2622ca1ebea","resolution":{"observed_at":"2026-08-07T12:35:39.360996Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:43.768232Z","title":null,"venue":null,"work_id":"f81480d2-e160-4190-bc02-053bc5ebc247","year":null},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.236947Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:ef16314b3cd07953fcc24050cda3e4cf821419347d2323e59d5c897158b34d00","observation_id":"e8307cc4-7680-4d84-af41-1e43b0a4df8c","resolution":{"observed_at":"2026-08-07T12:35:43.845354Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:42.129976Z","title":"Thutmose tagger: Single-pass neural model for inverse text normalization,","venue":null,"work_id":"7e61423f-01ab-4f17-b387-7725df2b8430","year":2022},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.975390Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:e0ea797755caedad0f4771fa412930438242a14450a00275bb5345d426323bc9","observation_id":"fdad31e0-2ba6-43bf-b212-8d36319a11ff","resolution":{"observed_at":"2026-08-07T12:35:42.207683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:43.402006Z","title":"B” signifies the beginning of a desig- nated category, “I","venue":null,"work_id":"c5dd18b8-7506-4643-b840-3c59d0639e49","year":null},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.411510Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:2137ad09bfa3fa5aa52abb507b4678e5cd08b7e8e14501f9688d1363209ef280","observation_id":"c695b5db-e34b-4591-89a0-6c85c24da005","resolution":{"observed_at":"2026-08-07T12:35:43.481379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:43.217339Z","title":"By adapting a pretrained model for streaming applica- tions, we addressed the inherent limitations of traditional ITN methods, particularly in real-time scenarios","venue":null,"work_id":"b03b7905-3fa2-4e9f-b03f-402cdeab784f","year":null},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.502662Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:0c4dd2dce3c7429eb6144ab2c5c1bb3620b75cdb1185370eaf1e8411bc0718b1","observation_id":"672bc94c-5a95-4318-94ec-23fe9832a550","resolution":{"observed_at":"2026-08-07T12:35:43.277961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:43.003384Z","title":"Adapitn: A fast, reliable, and dynamic adaptive inverse text normalization,","venue":null,"work_id":"88bc28fc-9e93-46b9-be6a-b2c95034eb1b","year":2023},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.598017Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:ee5b548feb91d2e0e268898bf68efd9916ef8ce1e591aa197645e0f2ec6b4638","observation_id":"7a84fabf-f55c-4525-8be9-862c69cdef67","resolution":{"observed_at":"2026-08-07T12:35:43.089086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:42.810803Z","title":"Improving neural text normalization with data augmentation at character- and morphological levels,","venue":null,"work_id":"01a1bda1-d082-4e21-9a92-b3f5f1835850","year":2017},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.670047Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:db5c57d80509d53fb054d67a3d22b64c66bb4d45ceee96f6d14899ddb8cd7a9b","observation_id":"a5287df6-e7bf-47be-b779-27c75202be78","resolution":{"observed_at":"2026-08-07T12:35:42.897792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:42.558378Z","title":"Spoken-to-written text conversion with large language model,","venue":null,"work_id":"967ba9d5-5068-4433-8a68-7b49004091f4","year":2024},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.756490Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:a3c23feda709accf50e866d1e5a89ac531ba1d9269c6940fc65935649857fdf4","observation_id":"6d674e6b-36d7-4877-9772-9d0073472751","resolution":{"observed_at":"2026-08-07T12:35:42.649377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:42.310421Z","title":"Neu- ral inverse text normalization,","venue":null,"work_id":"2c7c651e-6fad-4823-b8cc-75e43e8319b9","year":2021},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.873233Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:b269ba6d7b7a02da1d095ac272e0c0c6bc3190b4842d85e9be733914a91c8c14","observation_id":"ce8d1d3e-c3c7-4376-a324-9678e8c5de8d","resolution":{"observed_at":"2026-08-07T12:35:42.400417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:38.675663Z","title":"BERT: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.675663Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:836e1cb5f23a7bf0d17e1d9adacd1ae9de462d91536ad4677c69fff015d52519","observation_id":"0bdd017a-b05e-4173-a233-3c26f0cce7b6","resolution":{"observed_at":"2026-08-07T12:35:38.675663Z","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":"2026-08-07T12:35:41.966058Z","title":"Four- in-one: a joint approach to inverse text normalization, punctua- tion, capitalization, and disfluency for automatic speech recog- nition,","venue":null,"work_id":"ce722c64-9cbf-4b95-8097-f231c14697d0","year":2022},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.088225Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:df7f28db56414b44bf925a3658507f84bbc8d753b337804a692e243cb982fcdb","observation_id":"0551f1f3-5d9c-4e5a-8b17-a08ed7f61af0","resolution":{"observed_at":"2026-08-07T12:35:42.055313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:43.557279Z","title":"welcome” may be tokenized as two distinct tokens, “wel","venue":null,"work_id":"79e7f376-ff9f-4180-8a21-5d2f6456aaf4","year":null},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:37.312947Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:d559917766f963d9ae3c297d5e5ed197249ec66e634d2e1583326a9c40d3d92d","observation_id":"d3a707e5-b481-424e-9f25-849e2134176a","resolution":{"observed_at":"2026-08-07T12:35:43.661388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:41.738185Z","title":"A mostly data-driven approach to inverse text normalization,","venue":null,"work_id":"66dcaf58-b647-416a-aa81-cee8743027e7","year":2017},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.172893Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:e4279cc44661d711f098aaa7f78875c6757cb87c0e2341acc6affa1d2e0f6c5c","observation_id":"ede470b1-4bd3-47a0-808a-4565834b92be","resolution":{"observed_at":"2026-08-07T12:35:41.838717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:41.521325Z","title":"Scaling neural ITN for numbers and temporal expressions in Tamil: Findings for an agglutinative low-resource language,","venue":null,"work_id":"0a2f5458-804d-449e-8098-c3e0c3d57244","year":2023},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.271558Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:830feed23b98969c80fd10cf46fb1abfa26ac190ff71a004f53d29e82f462e2e","observation_id":"40445fa2-96cb-4265-bde0-3e370a6acb40","resolution":{"observed_at":"2026-08-07T12:35:41.640095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:41.265690Z","title":"Improving data driven inverse text normalization using data augmentation and machine translation,","venue":null,"work_id":"35e975d0-a12e-4f22-b6c4-eda20da7b5d4","year":2022},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.363536Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:719af7a5697b6710b341f68691ac7d9c0700171f609701474a1978088770f4b5","observation_id":"a0c1cb45-7536-46cd-bf8a-f4060b284027","resolution":{"observed_at":"2026-08-07T12:35:41.369345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:41.024481Z","title":"Streaming, fast and accurate on-device inverse text normalization for automatic speech recognition,","venue":null,"work_id":"96ac4ff0-420b-452c-9003-1535d0a8d101","year":2022},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.436393Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:b80018e26a7dd2c68db351b8be256f98e08634faea1699bd986fcc2a934c2d89","observation_id":"c31a2889-bbe5-41cf-811a-af4e23159ae1","resolution":{"observed_at":"2026-08-07T12:35:41.159162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:40.671405Z","title":"Improving streaming speech recognition with time-shifted contextual attention and dynamic right context mask- ing,","venue":null,"work_id":"cb0d357f-592e-471f-94f7-0bfcb231e179","year":2024},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.568715Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:3eb10fdecd8ba3e54bf073169209626e4e96e6099eb21c630a918b00806f0adf","observation_id":"a3751fa2-ac6e-412a-bbaf-7024f917cbd8","resolution":{"observed_at":"2026-08-07T12:35:40.840146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:38.751568Z","title":"PhoBERT: Pre-trained language models for Vietnamese,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.751568Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:45d7cbe954a02c55e3f99d8c85acfc95e5c890d149543ae88a9468d3ba208a39","observation_id":"491c7961-103f-4b06-9230-64c845761b73","resolution":{"observed_at":"2026-08-07T12:35:38.751568Z","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":"2026-08-07T12:35:40.359960Z","title":"Mohri, Weighted Finite-State Transducer Algorithms","venue":null,"work_id":"8f8f9151-6c95-44e3-be20-e3ebbb5e9703","year":2004},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.842737Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:3b24aed15b9fcf37f5d56cb4b8348a3c303fd8f2feb924f24fd2643b05fb8746","observation_id":"e8d6bd8a-d3c1-42d2-b60c-0845bc3bddf3","resolution":{"observed_at":"2026-08-07T12:35:40.497358Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:40.042424Z","title":"Chunkformer: Masked chunking conformer for long-form speech transcription,","venue":null,"work_id":"99579114-0a00-4398-b108-8d3b198f01f9","year":2025},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:38.942370Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:738aced0d9bf856bf1565a36dd4008eccb103273b518af09846386d2103693a5","observation_id":"d568cb23-b64c-4b3f-86dc-b3aaa0ff059f","resolution":{"observed_at":"2026-08-07T12:35:40.167400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:39.855630Z","title":"Confidence intervals for evaluation in machine learning","venue":null,"work_id":"58a4f595-6cc5-4bda-96ed-a7d6ddc28499","year":null},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:39.014566Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:423b86e5a2aa86379315c7f2dd3dc11786bcb07ae2861dca3501e63cbb00945e","observation_id":"5257f8a2-e6aa-43ae-852c-1400649a1130","resolution":{"observed_at":"2026-08-07T12:35:39.920979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:39.659478Z","title":"Nemo (inverse) text normalization: From development to production,","venue":null,"work_id":"a56a0270-7bcc-4163-935c-3e2b06951976","year":2021},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:39.088132Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:d31716456722bc6806d52a98c691ca38ede2f46ad04886044b31e3cb8d60f675","observation_id":"4cb295e3-7835-4a7b-a297-a43928f03410","resolution":{"observed_at":"2026-08-07T12:35:39.720307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:39.463301Z","title":"Variable attention masking for configurable transformer trans- ducer speech recognition,","venue":null,"work_id":"eb4737df-b43f-4fcb-8c66-3493bb657116","year":2023},"citing_paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:35:39.165139Z"},"links":{"citing_paper":"/paper/2505.24229"},"observation_digest":"sha256:8551b87f73f5371a1a510569b55d8d92da47aaef519c2e2cadd64efde4dde927","observation_id":"49eb7b24-65be-46be-b2b6-2fa04d352bb7","resolution":{"observed_at":"2026-08-07T12:35:39.537175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.24229","last_updated":"2025-05-30T05:41:03Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T12:26:11.710046Z","submitted_at":"2025-05-30T05:41:03Z","title":"Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":20},"total_outbound_references":25},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2505.24229."}