{"as_of":"2026-08-10T09:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5fbd8a76c7850fca857f76d2bdae8b3d214027974347a1d74812fe60a77943c2","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T20:10:32.806282Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T00:23:29.977813Z","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":"2026-05-18T02:00:39.965426Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"cited_work":{"arxiv_id":"2502.00127","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.00127","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1544a696-91e0-463c-8723-32977adf9773","year":2025},"citing_paper":{"arxiv_id":"2511.01680","last_updated":"2026-07-15T14:09:50Z","snapshot_observed_at":"2026-08-04T00:23:23.110922Z","submitted_at":"2025-11-03T15:42:32Z","title":"Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-18T01:56:50.978054Z"},"links":{"cited_paper":"/paper/2502.00127","citing_paper":"/paper/2511.01680"},"observation_digest":"sha256:457ad97c94df9038a266f128ddceee6401affe66e37664ab10b735073b9e8af5","observation_id":"c09f40a4-8500-4931-bcc1-a2a08584e441","resolution":{"observed_at":"2026-05-18T02:00:39.967921Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00127","snapshot_observed_at":"2026-08-04T00:23:29.977813Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01680","last_updated":"2026-07-15T14:09:50Z","snapshot_observed_at":"2026-08-04T00:23:23.110922Z","submitted_at":"2025-11-03T15:42:32Z","title":"Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach","version":4},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-04T00:23:29.977813Z"},"links":{"cited_paper":"/paper/2502.00127","citing_paper":"/paper/2511.01680"},"observation_digest":"sha256:abe30bfba9bc460dd66d4771b79ce7b9149849a4442c4e583658bd8581b99e60","observation_id":"4eedb172-729c-4e8b-ba19-7b5855550916","resolution":{"observed_at":"2026-08-04T00:23:29.977813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.00127/citation-record","integrity":"/paper/2502.00127/integrity","json":"/paper/2502.00127/citation-record.json","paper":"/paper/2502.00127"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T20:10:33.194050Z","title":"One pixel attack for fooling deep neural networks,","venue":null,"work_id":"674f4642-5376-4c3e-9ecc-e339500f9e3f","year":2019},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.727453Z"},"links":{"citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:597ffaa7b2c526aa80933f8142b55f92039ba49f1e8f77814216a7266a48de39","observation_id":"5b4f9b47-391e-4114-978e-6f75979a60c8","resolution":{"observed_at":"2026-08-09T20:10:33.202248Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1602.04938","last_updated":"2016-08-09T17:54:52Z","snapshot_observed_at":"2026-07-06T04:46:17.931363Z","submitted_at":"2016-02-16T08:20:14Z","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.04938","snapshot_observed_at":"2026-08-09T20:10:32.733356Z","title":"”why should i trust you?","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.733356Z"},"links":{"cited_paper":"/paper/1602.04938","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:66993599aaeae53c71b6aaa42c535d26d57ca2a6422a1799db472630bf9d1e00","observation_id":"665b226e-caf0-472c-bd81-64b3edb7fdb9","resolution":{"observed_at":"2026-08-09T20:10:32.733356Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.10263","last_updated":"2019-06-24T23:08:03Z","snapshot_observed_at":"2026-07-06T08:02:33.524166Z","submitted_at":"2019-06-24T23:08:03Z","title":"DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.10263","snapshot_observed_at":"2026-08-09T20:10:32.739873Z","title":"Dlime: A deterministic local interpretable model-agnostic explanations approach for computer-aided diagnosis systems,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.739873Z"},"links":{"cited_paper":"/paper/1906.10263","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:041ac057a1986c557de35edc17c16c1efc06750e2ad7015ca2c292a9ee5e6fc0","observation_id":"4d8a665e-af38-497a-bdf8-af09304e972f","resolution":{"observed_at":"2026-08-09T20:10:32.739873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.02685","last_updated":"2019-10-12T22:13:28Z","snapshot_observed_at":"2026-08-09T21:23:12.323244Z","submitted_at":"2017-04-10T02:23:57Z","title":"Learning Important Features Through Propagating Activation Differences","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.02685","snapshot_observed_at":"2026-08-09T20:10:32.746028Z","title":"Learning important features through propagating activation differences,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.746028Z"},"links":{"cited_paper":"/paper/1704.02685","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:00aca2e7ecba5a41272f61bd811af17e2838834832e050d034c2d7a435aaa5a2","observation_id":"73815af4-05a9-46db-90c7-7a20e3a5a11e","resolution":{"observed_at":"2026-08-09T20:10:32.746028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.01365","last_updated":"2017-06-13T01:52:38Z","snapshot_observed_at":"2026-07-06T05:32:18.507883Z","submitted_at":"2017-03-04T00:18:49Z","title":"Axiomatic Attribution for Deep Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.01365","snapshot_observed_at":"2026-08-09T20:10:32.752155Z","title":"Axiomatic attribution for deep networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.752155Z"},"links":{"cited_paper":"/paper/1703.01365","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:b74a249267a25852a52ef835c5a7ad28cba657a87749f2cc8ff382c16dfbe1c9","observation_id":"927d94e1-2872-437d-be14-d8f3c53dc986","resolution":{"observed_at":"2026-08-09T20:10:32.752155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.14269","last_updated":"2021-04-23T16:45:16Z","snapshot_observed_at":"2026-07-06T10:08:59.640325Z","submitted_at":"2020-10-27T13:10:51Z","title":"Leveraging speaker attribute information using multi task learning for speaker verification and diarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.14269","snapshot_observed_at":"2026-08-09T20:10:32.757999Z","title":"Leveraging speaker attribute information using multi task learning for speaker verification and diarization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.757999Z"},"links":{"cited_paper":"/paper/2010.14269","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:b1e91bb7d8cfa833ab3d8b15b175cd4eea43fae48d7b8e41e0060cafd0ad2eb2","observation_id":"9e67e06d-e4fd-441d-8879-ed54c7800046","resolution":{"observed_at":"2026-08-09T20:10:32.757999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19796","last_updated":"2024-05-30T08:04:28Z","snapshot_observed_at":"2026-07-06T18:22:31.384923Z","submitted_at":"2024-05-30T08:04:28Z","title":"Explainable Attribute-Based Speaker Verification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19796","snapshot_observed_at":"2026-08-09T20:10:32.763898Z","title":"Explainable attribute-based speaker verification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.763898Z"},"links":{"cited_paper":"/paper/2405.19796","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:e921686335c3b19be6e6b13fa7ee0554f050dd9e41bcce4548f3021652dd8e6e","observation_id":"9159050e-be9e-4ff1-ac5d-123ad43fa879","resolution":{"observed_at":"2026-08-09T20:10:32.763898Z","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":"2026-08-09T20:10:33.277552Z","title":"Towards monosemanticity: Decomposing language models with dictionary learning,","venue":null,"work_id":"2889e8f1-4f02-4efd-ae40-800b239fd863","year":2023},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.769041Z"},"links":{"citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:1b18b1016194fe23f817a24ada91e9e87dc88025607c865005c16a14c8106ad8","observation_id":"894797c4-4280-41d3-91ef-903123e3bd0b","resolution":{"observed_at":"2026-08-09T20:10:33.282820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T20:10:33.260434Z","title":"Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet,","venue":null,"work_id":"c90a7f7f-e281-491f-b688-7704d5d26a58","year":2024},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.774112Z"},"links":{"citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:7b05aa2e23f1b3ef53eb61777bc3d5f9cef5cbd08efd916b5809307e405220fc","observation_id":"c0f6393c-848d-41fb-a478-fffe6b3efbbc","resolution":{"observed_at":"2026-08-09T20:10:33.265790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04093","last_updated":"2024-06-06T14:10:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-06T14:10:12Z","title":"Scaling and evaluating sparse autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04093","snapshot_observed_at":"2026-08-09T20:10:32.778903Z","title":"Scaling and evaluating sparse autoencoders,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.778903Z"},"links":{"cited_paper":"/paper/2406.04093","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:47674378e66687f1699909e96e7127e2edc775eaf676a919ecbfc419f01113ba","observation_id":"06ea5279-a7d8-479e-b8a5-a33b986d2d74","resolution":{"observed_at":"2026-08-09T20:10:32.778903Z","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":"2026-08-09T20:10:33.243558Z","title":"Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2,","venue":null,"work_id":"2c2feeb1-45eb-43ff-8ea4-5e86912da65c","year":null},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.784094Z"},"links":{"citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:2739e9c74f9072fdd39738c76236b0dca5fdaa0a5f1918cdad20e8a68e035f66","observation_id":"468649fd-31d0-41be-a6ef-1d051061caf0","resolution":{"observed_at":"2026-08-09T20:10:33.249319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04410","last_updated":"2021-10-08T23:49:42Z","snapshot_observed_at":"2026-08-06T03:08:15.868993Z","submitted_at":"2021-10-08T23:49:42Z","title":"TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04410","snapshot_observed_at":"2026-08-09T20:10:32.795144Z","title":"Titanet: Neural model for speaker representation with 1d depth-wise separable convolutions and global context,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.795144Z"},"links":{"cited_paper":"/paper/2110.04410","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:41a30eda653407800032513ac558ca5ac631c28a0e0a62703966b1fa65647f45","observation_id":"2b7594a7-59f6-4735-b6aa-48d17cafefbc","resolution":{"observed_at":"2026-08-09T20:10:32.795144Z","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":"2026-08-09T20:10:33.226415Z","title":"NeMo: a toolkit for Conversational AI and Large Language Models","venue":null,"work_id":"9a631c1d-626e-446f-ba0b-c8ed21b38d4b","year":null},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.800288Z"},"links":{"citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:87345c24eee08a63ed705fcd1202fc7348773bb384d0625c97bd8c68bbd5b226","observation_id":"9b8782cd-32b0-42ac-8082-89382000a6c7","resolution":{"observed_at":"2026-08-09T20:10:33.231714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T20:10:32.806282Z","title":"Robust speech recognition via large-scale weak supervi- sion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.806282Z"},"links":{"citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:6db6ccf0180452e75871d47cb7185b91bfefc9509f65029541f1e54723ab1fa2","observation_id":"3c6c56c5-58c1-4b90-a799-88fa412ff859","resolution":{"observed_at":"2026-08-09T20:10:32.806282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.05147","last_updated":"2024-08-19T07:51:05Z","snapshot_observed_at":"2026-08-04T11:44:14.524984Z","submitted_at":"2024-08-09T16:06:42Z","title":"Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.05147","snapshot_observed_at":"2026-08-09T20:10:32.789781Z","title":"Available: https://arxiv.org/abs/2408.05147","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-09T20:10:32.789781Z"},"links":{"cited_paper":"/paper/2408.05147","citing_paper":"/paper/2502.00127"},"observation_digest":"sha256:7dc744f0165d18183f943b5b075e6036c52daccbd57a8f55458ad99d0179614f","observation_id":"295d5822-a12f-4c41-9406-d985907c68e6","resolution":{"observed_at":"2026-08-09T20:10:32.789781Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.00127","last_updated":"2025-01-31T19:21:43Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T20:03:58.757044Z","submitted_at":"2025-01-31T19:21:43Z","title":"Sparse Autoencoder Insights on Voice Embeddings"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":4},"total_outbound_references":15},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 2 inbound Pith citation observations for arXiv:2502.00127."}