{"as_of":"2026-08-16T18:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5ca0b24aa2a95587d37a867efa3b446859ac4fae2fa56814af8469baac3d22dc","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:52:39.815038Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.23420/citation-record","integrity":"/paper/2505.23420/integrity","json":"/paper/2505.23420/citation-record.json","paper":"/paper/2505.23420"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:52:09.768731Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:09.768731Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:2081e1169dc3e8682dbb7cf0bbc3b0abbd30342c2efac3475695cd2d729a92ed","observation_id":"d658d7a0-ea27-4393-b9ed-b1c2615698b4","resolution":{"observed_at":"2026-08-07T12:52:09.768731Z","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":"2026-08-07T12:52:36.815921Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:36.815921Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:aed83a2e7c4925f9669c8e2f594b2cf5c290dbf6843fdf443f0b8e33c5b050d1","observation_id":"f44b55dc-a376-4f41-8d31-a63d88a89b01","resolution":{"observed_at":"2026-08-07T12:52:36.815921Z","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":"2026-08-07T12:52:36.967501Z","title":"Bengio, P","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:36.967501Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:270aa430f39ebe0b0aa4e8e66c2284ddf6edbb26501503a16abb07ee23820d0e","observation_id":"5255b931-0221-4a28-85c2-ddebf3029714","resolution":{"observed_at":"2026-08-07T12:52:36.967501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11596","last_updated":"2023-10-25T03:52:07Z","snapshot_observed_at":"2026-08-16T15:06:35.991757Z","submitted_at":"2023-08-22T17:44:18Z","title":"SeamlessM4T: Massively Multilingual & Multimodal Machine Translation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11596","snapshot_observed_at":"2026-08-07T12:52:37.139885Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.139885Z"},"links":{"cited_paper":"/paper/2308.11596","citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:cd7748dd67c1d899c11e18c9e7ca134200e44e153b1aa928a98824a01fb9abae","observation_id":"815fcb30-b8b7-4373-b947-25cb91b1f6a8","resolution":{"observed_at":"2026-08-07T12:52:37.139885Z","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":"2026-08-07T12:52:37.293692Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.293692Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:a98ec29da55dba27b826f4fb9de7065add2480f9e59744abd1c0c48d9ec63ac6","observation_id":"eb334cf7-c813-450a-b939-21b87a228888","resolution":{"observed_at":"2026-08-07T12:52:37.293692Z","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":"2026-08-07T12:52:43.086175Z","title":null,"venue":null,"work_id":"6def6b08-2374-4a55-a3db-8869ca4aac5e","year":2024},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.447431Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:c9ef66e356920f8c4cbfd1d5ddba07e13cf34e4a98c0ec71dcea95856a633c88","observation_id":"4cec7b9f-a451-4f6c-9748-675267e6e65e","resolution":{"observed_at":"2026-08-07T12:52:43.292422Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:42.752837Z","title":"Gomez, and J \\\"u rgen Schmidhuber","venue":null,"work_id":"443fd0e0-64d4-45b1-8d5b-117099671c04","year":2006},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.566917Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:885bf8b60910a753abdc8179b80b78d1f0c18c30ecd8fff4bb37e7dd4c7ce1d4","observation_id":"61938232-fd75-4864-8bd9-df1f0cd84dfe","resolution":{"observed_at":"2026-08-07T12:52:42.903210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:37.680548Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.680548Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:a9768a4264d5134f06819aae6dde0efe2a019bec589c572143dad036a6a1cf66","observation_id":"509b08d1-ef93-4493-a0f3-2a9ef51d0ba2","resolution":{"observed_at":"2026-08-07T12:52:37.680548Z","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":"2026-08-07T12:52:37.775712Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.775712Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:46369c44628ad7c105674993c21d2197704281e7d4c7633a22eb421050c92e99","observation_id":"cde2a3d9-8e60-4cb2-8649-721d0423d1a2","resolution":{"observed_at":"2026-08-07T12:52:37.775712Z","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":"2026-08-07T12:52:42.420613Z","title":null,"venue":null,"work_id":"e332e6b2-c6e7-4bbf-8407-c39b4afe2b56","year":2020},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.880479Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:0d946568e6b71a0b34730dedc76d66b92da9552c31d2d7a78745f931f3cf23b9","observation_id":"2808d637-25fe-4663-9eda-a6c06cdb2e1c","resolution":{"observed_at":"2026-08-07T12:52:42.564864Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:42.083225Z","title":"Kahn , M","venue":null,"work_id":"40cf0d40-47c8-44b3-b31d-7f337acbb900","year":2020},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:37.955698Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:68a7b19e06daf4f8caf990072c1b9bd19ea955bc60ff1a2f76dbd2f2f5ae054b","observation_id":"fb206f54-d1d5-45e9-b957-b0be252a9408","resolution":{"observed_at":"2026-08-07T12:52:42.260808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:41.870341Z","title":null,"venue":null,"work_id":"f63c030b-c7f0-4578-be4a-df4401762c85","year":2024},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.025651Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:77f2e8caa54d074372b3f07a7708631395cd76b4061460662e1d24c6051c1b33","observation_id":"27972bc5-d29b-488d-b601-a2fcaaee89d5","resolution":{"observed_at":"2026-08-07T12:52:41.936033Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:38.090135Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.090135Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:60cd708c20b2a9eddba0cbca99f3392d9574d09e67260c88355c174c21821844","observation_id":"fcf06636-8080-452f-9e1a-fa770e4a958f","resolution":{"observed_at":"2026-08-07T12:52:38.090135Z","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":"2026-08-07T12:52:41.626405Z","title":null,"venue":null,"work_id":"dbdf98b3-f3ba-4d66-8768-d5c63ff90835","year":2025},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.157188Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:a5ed50658751e7291b6696ad2348cc4dba40f61a89548ca7d8eab0157632e998","observation_id":"9b6fedc0-b760-4bff-8a21-5e21da7499ce","resolution":{"observed_at":"2026-08-07T12:52:41.737906Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.acl-long.200","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":null,"work_id":"aa5006c1-4104-4e63-97a9-f739937d7274","year":2024},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.271867Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:ec04cf03a79e4b50bc87fccff0b576479a3f19d35d71029eb21fbb80070b05cd","observation_id":"382e7022-07ef-441f-b9ff-a7365a10a5f6","resolution":{"observed_at":"2026-08-07T12:52:40.178208Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:41.353721Z","title":null,"venue":null,"work_id":"f0cb3d2e-bde9-4bfb-b6cb-d541c2f92578","year":2022},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.368053Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:49b4452906c8c5eef5948e9741394969870386c36d4c568088d63762d393e0d0","observation_id":"ef78a0b0-2851-42ac-9661-9283da2cddba","resolution":{"observed_at":"2026-08-07T12:52:41.478096Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:38.453081Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.453081Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:b420b783b41282fa3c3be661a780eb07c6c5ef2a4f01aef932d7e7c8c2546fce","observation_id":"c3b9495c-e5a3-4195-b607-0cffc4a67a88","resolution":{"observed_at":"2026-08-07T12:52:38.453081Z","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":"2026-08-07T12:52:38.531977Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.531977Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:93de16ce1fad13b20ad4ce1d61c0e06e7913d6c24bff8b97df5bc85207c87a64","observation_id":"74041dd6-4b48-4e4d-b0bb-d0c6a114d95e","resolution":{"observed_at":"2026-08-07T12:52:38.531977Z","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":"2026-08-07T12:52:41.162752Z","title":null,"venue":null,"work_id":"c7cec5c4-cb34-48bc-8ed9-7667788ba37d","year":2024},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.602716Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:e9255382d5c962c808eefe373e50f73c292ad263d1315e277dd30f5f9e63acd4","observation_id":"a005d3b2-b222-49d0-bd20-7a6042e7bd9a","resolution":{"observed_at":"2026-08-07T12:52:41.243835Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:40.919733Z","title":null,"venue":null,"work_id":"360abab5-02f2-47d3-85cc-6f06ba726024","year":2018},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.688824Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:5ff7e3495639de7bca7c499d33e84839f1f6224796379b2f060c8129a00efd20","observation_id":"e8b3e639-ceaf-4125-bea5-4fe7e73d9969","resolution":{"observed_at":"2026-08-07T12:52:41.045861Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:38.760309Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.760309Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:e381de2c1faa681bfa4ec243c0776b7bea76728ff5f01c2eee0ef5fce570e025","observation_id":"92291141-67f3-49fd-a050-141dd3fd52ae","resolution":{"observed_at":"2026-08-07T12:52:38.760309Z","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":"2026-08-07T12:52:40.628386Z","title":null,"venue":null,"work_id":"7dff53ac-4fa0-4525-99ce-a1851b0df369","year":2023},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.839623Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:2862b870128418728c3047090e798155e1140ddf19a184c0445225e7b1b44019","observation_id":"5ebfa85b-22f9-48d4-a093-fb3197f83613","resolution":{"observed_at":"2026-08-07T12:52:40.747138Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-07T12:52:38.904598Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:38.904598Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:8082aa48fc1bc27b2b0fcff4683eca2340bfef3e58f538ec0dc09889c16cef65","observation_id":"5246b08b-69b4-44d6-a150-17fc27b914ab","resolution":{"observed_at":"2026-08-07T12:52:38.904598Z","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":"2026-08-07T12:52:39.012770Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.012770Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:f3f65f715ec2b059946f0289530fe6c3fc502303310c8bc9d80c3eca783cebd3","observation_id":"e48b5980-1562-44ff-bcf0-f00f29a6c84a","resolution":{"observed_at":"2026-08-07T12:52:39.012770Z","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":"2026-08-07T12:52:39.103148Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.103148Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:7d0eaf92574fe24717ba10c6f7ef33098711242247d8d9329a148f8985e444f4","observation_id":"8e396704-1d2e-4cf0-9e55-08912c22c6da","resolution":{"observed_at":"2026-08-07T12:52:39.103148Z","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":"2026-08-07T12:52:39.184423Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.184423Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:0a801832f0cd00f5b9d15b427b471241d2ecd8670594ca1be9e1608354e576b1","observation_id":"6cdd0f30-3ab5-4dce-b5b6-fb7edb89973a","resolution":{"observed_at":"2026-08-07T12:52:39.184423Z","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":"2026-08-07T12:52:39.261212Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.261212Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:199f8891d506fc31bad1e20059483f205233eadeee087313b1cf84e5e0f3fd77","observation_id":"9ab3a582-a920-4a30-8295-09128fb4aa18","resolution":{"observed_at":"2026-08-07T12:52:39.261212Z","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":"2026-08-07T12:52:39.339802Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.339802Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:0acb55f5f57147131330978a6cc4f31fcc8cd34a38fbc48a5342070c15b13848","observation_id":"a7b57c44-42ed-4392-9ccd-5ac01d71281a","resolution":{"observed_at":"2026-08-07T12:52:39.339802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13784","last_updated":"2024-10-18T08:20:22Z","snapshot_observed_at":"2026-08-16T14:07:56.616333Z","submitted_at":"2024-03-20T17:47:08Z","title":"The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13784","snapshot_observed_at":"2026-08-07T12:52:39.434364Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.434364Z"},"links":{"cited_paper":"/paper/2403.13784","citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:b739ae836fac41de7b588da5a63c3edfbbb1f0a3787068f5b318dc3ae6c6f1d5","observation_id":"1c33a285-6a54-472f-946d-dfa3b7c60f07","resolution":{"observed_at":"2026-08-07T12:52:39.434364Z","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":"2026-08-07T12:52:39.518001Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.518001Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:1fad62d654755182f322c5650ec8a1aadc339d4a150c93aab5eb6314e560dc10","observation_id":"66bcd1d2-649e-47d3-bb26-b4bb2a49d09f","resolution":{"observed_at":"2026-08-07T12:52:39.518001Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.01037","last_updated":"2023-09-25T01:20:23Z","snapshot_observed_at":"2026-08-16T15:51:28.593814Z","submitted_at":"2023-03-02T07:47:18Z","title":"Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.01037","snapshot_observed_at":"2026-08-07T12:52:39.598918Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.598918Z"},"links":{"cited_paper":"/paper/2303.01037","citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:bdad6f30b516954c41a45ace31ef9a603b2a31832386a2a8d357d385af589c4a","observation_id":"a3224f44-2c51-44de-a819-e6ea0a11d0d2","resolution":{"observed_at":"2026-08-07T12:52:39.598918Z","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":"2026-08-07T12:52:39.686440Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.686440Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:292b26b10af1590c6c28dba5f14d0b4c88f0c15ef0bf9278d7e8c1b3c9172cb1","observation_id":"78b53180-e007-441f-82ec-adbafd0d9964","resolution":{"observed_at":"2026-08-07T12:52:39.686440Z","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":"2026-08-07T12:52:39.815038Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T12:52:39.815038Z"},"links":{"citing_paper":"/paper/2505.23420"},"observation_digest":"sha256:04cc40026bbbb59c32e59e5d81dea761c02c2bd2d2f3244b1606cfaa21b79316","observation_id":"bc7658b8-221e-4815-ae9e-6bb998e971fd","resolution":{"observed_at":"2026-08-07T12:52:39.815038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.23420","last_updated":"2025-05-29T13:10:57Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T17:56:05.942387Z","submitted_at":"2025-05-29T13:10:57Z","title":"The Warmup Dilemma: How Learning Rate Strategies Impact Speech-to-Text Model Convergence"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":1,"verified_fuzzy":2},"total_outbound_references":33},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.23420."}