{"as_of":"2026-08-21T09:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9f4dfd68a8eacfba85faf94b1467a2249c2ac4b90ca3d624012361daf02fe050","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":39,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:55:43.589516Z","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":"2026-07-09T21:16:34.237758Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"1804.07461","last_updated":"2019-02-22T23:53:34Z","snapshot_observed_at":"2026-08-16T09:50:11.319379Z","submitted_at":"2018-04-20T06:35:04Z","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-12T21:24:14.913907Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1804.07461"},"observation_digest":"sha256:b3e8d2831033022cf378bca739ee047e06aae16eaab38eeb25b32323bb3d39c7","observation_id":"62cde0dc-7632-42da-9e6a-e2fb60406040","resolution":{"observed_at":"2026-05-12T21:24:15.591032Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"1905.00537","last_updated":"2020-02-13T00:28:00Z","snapshot_observed_at":"2026-08-17T22:08:45.245439Z","submitted_at":"2019-05-02T00:41:50Z","title":"SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems","version":3},"reference_index":147,"source":"arxiv_source","source_observed_at":"2026-05-15T01:34:10.604864Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1905.00537"},"observation_digest":"sha256:5b0b0a08d2a80eef5cbe6fa441ccb7e2b372dca3e0d2d8e82b6151176af9963e","observation_id":"5e81cfa2-e8a4-4634-8a2b-c7395bd2cb82","resolution":{"observed_at":"2026-05-15T01:34:10.786302Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-08-16T14:33:50.657682Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-09T04:47:43.784327Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1907.11692"},"observation_digest":"sha256:64a915f6eea7406bdb766588643fe564b75569481a7b8980ffabb8f21e712a59","observation_id":"a47ce014-e42f-4cb9-b107-931ebce220dd","resolution":{"observed_at":"2026-05-09T04:47:44.491477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-14T13:58:53.274383Z","title":"Neural network acceptability judgments","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.04211","last_updated":"2020-02-07T17:44:52Z","snapshot_observed_at":"2026-08-17T12:24:48.299916Z","submitted_at":"2019-08-12T15:48:34Z","title":"On Identifiability in Transformers","version":4},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-14T13:58:53.274383Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1908.04211"},"observation_digest":"sha256:60eb3e7210df3ba682db9f1b874435b6bb324da3a0f8a20c872efd8075faf349","observation_id":"6927b9a2-8151-4cfb-8842-5a86d5c179b8","resolution":{"observed_at":"2026-08-14T13:58:53.274383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-14T13:42:20.402688Z","title":"Neural network acceptability judgments.arXiv preprint arXiv:1805.12471, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.04577","last_updated":"2019-09-27T05:44:38Z","snapshot_observed_at":"2026-08-18T02:54:24.584328Z","submitted_at":"2019-08-13T11:12:58Z","title":"StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T13:42:20.402688Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1908.04577"},"observation_digest":"sha256:54cd92f4f8357a7d8d1d88a56fecd2c2aced3a5316e9f0f4ac32084b363c681e","observation_id":"6d67b1f7-5ba4-4885-8734-c47f26d2f32b","resolution":{"observed_at":"2026-08-14T13:42:20.402688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-14T13:08:08.556068Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.07820","last_updated":"2020-08-07T08:06:18Z","snapshot_observed_at":"2026-08-19T09:37:12.933598Z","submitted_at":"2019-08-16T03:16:40Z","title":"Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T13:08:08.556068Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1908.07820"},"observation_digest":"sha256:4aeb888705b05429ad684ef6586ac17d6c5f4858a2eb69a720fe1c9fc772dfcd","observation_id":"2989673e-44be-48dd-ba9e-da4aadbbbd02","resolution":{"observed_at":"2026-08-14T13:08:08.556068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-14T10:47:14.385299Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.10423","last_updated":"2019-08-27T19:26:31Z","snapshot_observed_at":"2026-08-16T21:45:58.166010Z","submitted_at":"2019-08-27T19:26:31Z","title":"Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-14T10:47:14.385299Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1908.10423"},"observation_digest":"sha256:37c2f883cd5bc6d5fa50e2005140ed9260c20beb4cc49c914f620c20ba3ddeb8","observation_id":"0e17075b-b0e0-402d-bb5d-86f4e9a2a6f4","resolution":{"observed_at":"2026-08-14T10:47:14.385299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-14T04:59:31.965263Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02209","last_updated":"2020-02-04T09:43:22Z","snapshot_observed_at":"2026-08-20T10:03:59.684916Z","submitted_at":"2019-09-05T04:47:10Z","title":"Semantics-aware BERT for Language Understanding","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-14T04:59:31.965263Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1909.02209"},"observation_digest":"sha256:24859f459e8c635472969ab77e61a3366e6c5851da91769ca707d63c52e4f6a8","observation_id":"973b62cf-8b9d-4227-aaab-8490481578c9","resolution":{"observed_at":"2026-08-14T04:59:31.965263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-14T04:50:03.443285Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02597","last_updated":"2019-09-19T18:13:06Z","snapshot_observed_at":"2026-08-14T19:36:33.539973Z","submitted_at":"2019-09-05T18:58:51Z","title":"Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-14T04:50:03.443285Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1909.02597"},"observation_digest":"sha256:6bb62809ab8f2c73ff9bb672877dc9014791c2d4f87d8ed8d4db1e34de2cb927","observation_id":"5effc7cf-f301-4326-9326-08b324eb5037","resolution":{"observed_at":"2026-08-14T04:50:03.443285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"1909.11942","last_updated":"2020-02-09T03:00:18Z","snapshot_observed_at":"2026-08-18T01:27:11.590348Z","submitted_at":"2019-09-26T07:06:13Z","title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","version":6},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-13T12:26:58.015594Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1909.11942"},"observation_digest":"sha256:38de61e46156517e7912abdf289ffdc1cd36b31cc7d36b220b271f8d22bd6429","observation_id":"e3e36867-c1b9-46a2-8274-49604f21458a","resolution":{"observed_at":"2026-05-13T12:26:58.126204Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"1910.03771","last_updated":"2020-07-14T03:42:34Z","snapshot_observed_at":"2026-07-06T08:27:58.343233Z","submitted_at":"2019-10-09T03:23:22Z","title":"HuggingFace's Transformers: State-of-the-art Natural Language Processing","version":5},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-11T14:53:58.963468Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1910.03771"},"observation_digest":"sha256:c6b9e99562b13ce62ec7f6ab5ff34dcb9fb2c477085335297d8cf78cbc480b93","observation_id":"4c30a404-4683-4192-8f31-3770bfeb9542","resolution":{"observed_at":"2026-05-11T14:53:59.351203Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"1910.13461","last_updated":"2019-10-29T18:01:00Z","snapshot_observed_at":"2026-07-06T08:33:12.534026Z","submitted_at":"2019-10-29T18:01:00Z","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-13T00:14:58.134513Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/1910.13461"},"observation_digest":"sha256:799735216cb2e2262570521fa33ccf1fc49a4296a32055f634a4aadf76c4f957","observation_id":"42bb24f5-4697-4f22-bb05-cfabbc907bef","resolution":{"observed_at":"2026-05-13T00:14:58.163693Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2003.10555","last_updated":"2020-03-23T21:17:42Z","snapshot_observed_at":"2026-08-11T12:55:51.308870Z","submitted_at":"2020-03-23T21:17:42Z","title":"ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-16T10:26:47.593122Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2003.10555"},"observation_digest":"sha256:3acd003b412902c0628ae34dd250b0975cf6320a9d03a755dc77e803b3c6c9af","observation_id":"e194d56d-7b41-4ece-a038-d9c7b57ebdaf","resolution":{"observed_at":"2026-05-16T10:26:47.647143Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-20T11:47:17.477107Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-09T05:01:39.906340Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2106.09685"},"observation_digest":"sha256:2c6ed0c6dc4e96d82e54cab91f6dbc3cdb398c2a0a87ddc730f8d19c5aacdbbc","observation_id":"aacf01b6-d9a8-4cd4-846d-32280a01a86c","resolution":{"observed_at":"2026-05-09T05:01:40.825462Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2205.01068","last_updated":"2022-06-21T17:04:40Z","snapshot_observed_at":"2026-08-06T03:13:37.403059Z","submitted_at":"2022-05-02T17:49:50Z","title":"OPT: Open Pre-trained Transformer Language Models","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-10T20:53:16.720145Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2205.01068"},"observation_digest":"sha256:93094daf030c6576eb528e8ae22b550e2f7539f47212ab388394351f51076b63","observation_id":"702c6908-308b-40e4-8b73-9b63c904e4ec","resolution":{"observed_at":"2026-05-10T20:53:17.222272Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-12T13:58:20.832836Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.15804","last_updated":"2024-11-24T12:21:14Z","snapshot_observed_at":"2026-08-18T12:58:11.760692Z","submitted_at":"2024-11-24T12:21:14Z","title":"LoRA-Mini : Adaptation Matrices Decomposition and Selective Training","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-12T13:58:20.832836Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2411.15804"},"observation_digest":"sha256:9ba064808133588f138bfbd433bc6b6ebe11973fe3b2f1f57263759ee2deb4a8","observation_id":"de7fcc87-342a-4b3a-bb3a-b8d1fbb31a47","resolution":{"observed_at":"2026-08-12T13:58:20.832836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-12T13:29:09.439000Z","title":"Neural network acceptability judgments","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16796","last_updated":"2025-08-25T16:33:35Z","snapshot_observed_at":"2026-08-16T09:24:44.109618Z","submitted_at":"2024-11-25T09:58:51Z","title":"HeteroTune: Efficient Federated Learning for Large Heterogeneous Models","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-12T13:29:09.439000Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2411.16796"},"observation_digest":"sha256:fd9aafef376d63e6b4e1de5e2d144cb5bac4d1507bfad675e383f90bb3c3fd8d","observation_id":"9dea8aa5-1fcd-4795-82b6-d9c2de06451b","resolution":{"observed_at":"2026-08-12T13:29:09.439000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-11T22:06:28.521305Z","title":"Neural network acceptability judgments","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03881","last_updated":"2025-03-04T04:28:19Z","snapshot_observed_at":"2026-08-15T23:37:24.568382Z","submitted_at":"2024-12-05T05:29:19Z","title":"Weak-to-Strong Generalization Through the Data-Centric Lens","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T22:06:28.521305Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2412.03881"},"observation_digest":"sha256:79806ce031b3187a24ec75ad20060f908328c02944a6cdad16a99bf62214d167","observation_id":"53eaa213-3f9a-44e9-97e9-f71cdb38f441","resolution":{"observed_at":"2026-08-11T22:06:28.521305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-11T19:55:43.889822Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.06245","last_updated":"2025-05-20T03:17:07Z","snapshot_observed_at":"2026-08-18T14:35:42.552159Z","submitted_at":"2024-12-09T06:37:35Z","title":"A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:43.889822Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2412.06245"},"observation_digest":"sha256:0150126494bea2130b7fe05a698736addd19b3de14f1379e6afc997c7e28af68","observation_id":"09919709-2096-41a3-b227-d0b6617d62c8","resolution":{"observed_at":"2026-08-11T19:55:43.889822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-10T20:56:43.897876Z","title":"Neural network acceptability judgments","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.06848","last_updated":"2025-07-18T17:52:45Z","snapshot_observed_at":"2026-08-16T00:49:53.156246Z","submitted_at":"2025-01-12T15:34:24Z","title":"A General Framework for Inference-time Scaling and Steering of Diffusion Models","version":5},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T20:56:43.897876Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2501.06848"},"observation_digest":"sha256:b0cf8d7ff4215c943635c1c8e3cc14f5b9db799797234e0888d58c74d1a5c3bf","observation_id":"09ea29ae-43e2-4c64-99c0-20fb8c43146c","resolution":{"observed_at":"2026-08-10T20:56:43.897876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-09T14:52:24.312828Z","title":"Neural network acceptability judgments","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.01628","last_updated":"2025-07-10T04:29:17Z","snapshot_observed_at":"2026-08-18T04:59:48.653004Z","submitted_at":"2025-02-03T18:57:17Z","title":"Harmonic Loss Trains Interpretable AI Models","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T14:52:24.312828Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2502.01628"},"observation_digest":"sha256:8d5e4f82fb94d0e4192ddf61d9c4f81a8e593afb1d0b00640f1313fc1b91d9df","observation_id":"600f3437-2ef5-41e2-9932-cfbdf4ce259c","resolution":{"observed_at":"2026-08-09T14:52:24.312828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-08T20:54:10.631118Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04958","last_updated":"2025-02-07T14:22:35Z","snapshot_observed_at":"2026-08-18T18:29:33.838895Z","submitted_at":"2025-02-07T14:22:35Z","title":"SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T20:54:10.631118Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2502.04958"},"observation_digest":"sha256:9fe76d8cfcbb7210e92b2a6d5604098c0462ea997a7ff639eb5919c5c806c7aa","observation_id":"0254b9ec-3565-4b91-8db5-7bf555c043f3","resolution":{"observed_at":"2026-08-08T20:54:10.631118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2504.16155","last_updated":"2026-05-07T09:59:28Z","snapshot_observed_at":"2026-08-19T10:57:08.068728Z","submitted_at":"2025-04-22T17:52:04Z","title":"PRIMETIME : Limits of LLMs in Temporal Primitives","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-22T18:36:48.376877Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2504.16155"},"observation_digest":"sha256:cdfdccc1a9f1449a0bc766ae736c0d8670696631b7f49fc511a87226375ea6f2","observation_id":"b15a6572-a69e-4b56-b3b0-90f329ac5c5d","resolution":{"observed_at":"2026-05-22T18:36:59.086711Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-15T20:55:43.589516Z","title":"Neural network acceptability judgments.CoRR abs/1805.12471, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.11586","last_updated":"2025-05-16T17:59:53Z","snapshot_observed_at":"2026-08-18T19:47:29.654941Z","submitted_at":"2025-05-16T17:59:53Z","title":"The Ripple Effect: On Unforeseen Complications of Backdoor Attacks","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T20:55:43.589516Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2505.11586"},"observation_digest":"sha256:f3dd5efff60f92f2b9f7c689d3f378ee8950d53ab0f54dc0b51efc4d3b2d9ba0","observation_id":"b1bc142d-d17d-48a6-a4c3-d20793e2c83a","resolution":{"observed_at":"2026-08-15T20:55:43.589516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-15T20:31:40.332787Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.12871","last_updated":"2025-05-19T08:57:08Z","snapshot_observed_at":"2026-08-19T09:18:50.117245Z","submitted_at":"2025-05-19T08:57:08Z","title":"Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-15T20:31:40.332787Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2505.12871"},"observation_digest":"sha256:ce60f19235a9bfa45874164eec621b274ac4be449561e7df86625195b7ceaa17","observation_id":"516e593b-35b5-4d4e-8022-29ea2b71cb22","resolution":{"observed_at":"2026-08-15T20:31:40.332787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-07T13:03:13.700940Z","title":"arXiv preprint 1805.12471","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.22934","last_updated":"2026-05-28T18:21:08Z","snapshot_observed_at":"2026-08-18T13:48:01.914436Z","submitted_at":"2025-05-28T23:28:12Z","title":"Unraveling LoRA Interference: Orthogonal Subspaces for Robust Model Merging","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-07T13:03:13.700940Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2505.22934"},"observation_digest":"sha256:a1d224197ab18f600a3da871804af691fb1197d17e8022f19f575b4b74b3fef2","observation_id":"099b5deb-fa5d-498b-a030-d986393a59b0","resolution":{"observed_at":"2026-08-07T13:03:13.700940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-07T10:29:44.993184Z","title":"Neural network acceptability judgments","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.05166","last_updated":"2025-06-06T01:35:43Z","snapshot_observed_at":"2026-08-17T20:25:49.693988Z","submitted_at":"2025-06-05T15:43:34Z","title":"Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T10:29:44.993184Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2506.05166"},"observation_digest":"sha256:8c838c9f5be3eee77d8627a6fe7035732ff5b71e48fcd8c669d3ec9dff363d0d","observation_id":"26a9f4e5-0caa-4643-80c8-0ee51cb33f37","resolution":{"observed_at":"2026-08-07T10:29:44.993184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-15T19:07:06.941728Z","title":"arXiv preprint arXiv:1805.12471 (2019) 83","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.17700","last_updated":"2025-06-21T12:25:37Z","snapshot_observed_at":"2026-08-18T09:26:29.102965Z","submitted_at":"2025-06-21T12:25:37Z","title":"The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T19:07:06.941728Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2506.17700"},"observation_digest":"sha256:e9b705e4cec6e8b911dc8ea307af50c9278a56c3b444a9debd66f07dc83767d6","observation_id":"62133c6a-1246-493c-b6fa-ad151b2a17cf","resolution":{"observed_at":"2026-08-15T19:07:06.941728Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-05T13:17:08.284180Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.00820","last_updated":"2025-08-31T12:35:12Z","snapshot_observed_at":"2026-08-19T13:36:20.555648Z","submitted_at":"2025-08-31T12:35:12Z","title":"Unlocking the Effectiveness of LoRA-FP for Seamless Transfer Implantation of Fingerprints in Downstream Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-05T13:17:08.284180Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2509.00820"},"observation_digest":"sha256:04f3005d3ea01801d3b35d63ed1014c3599ff529b3f8336f92b6caaaa57de84d","observation_id":"b8ee5b01-8fdd-4f66-a609-acde727c652e","resolution":{"observed_at":"2026-08-05T13:17:08.284180Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-04T19:43:37.997828Z","title":"Adina Williams, Nikita Nangia, and Samuel R Bow- man","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.09119","last_updated":"2025-09-11T03:07:05Z","snapshot_observed_at":"2026-08-21T04:28:38.711082Z","submitted_at":"2025-09-11T03:07:05Z","title":"Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T19:43:37.997828Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2509.09119"},"observation_digest":"sha256:a08f2b7b9920d664702ba8aaed5286ccca8b18d2e8103195b5c634d0fe112b8f","observation_id":"34db74f8-349a-420f-9e8f-527b2237dc6e","resolution":{"observed_at":"2026-08-04T19:43:37.997828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2509.18629","last_updated":"2026-04-22T19:36:04Z","snapshot_observed_at":"2026-08-11T04:07:19.888272Z","submitted_at":"2025-09-23T04:29:26Z","title":"HyperAdapt: Simple High-Rank Adaptation","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-18T13:44:09.263459Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2509.18629"},"observation_digest":"sha256:c3c5ad368ab6e23717dd8dd0b2f23a127120a8391ab088209f218c1682f57f4f","observation_id":"f39b41d6-d5c7-4a4e-b3ff-3c6d906ef6bc","resolution":{"observed_at":"2026-05-18T13:46:25.936427Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2604.09615","last_updated":"2026-03-14T12:34:27Z","snapshot_observed_at":"2026-08-11T16:55:00.704355Z","submitted_at":"2026-03-14T12:34:27Z","title":"Calibrating Microgrid Simulations for Energy-Aware Computing Systems","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-05-15T12:03:17.862705Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2604.09615"},"observation_digest":"sha256:91b8733c183ff05484b313b840c602bbe8fbb0364b8c6e267f63e2152813640f","observation_id":"ad1bd63c-066a-4881-9b45-8ad0f3484be9","resolution":{"observed_at":"2026-05-15T12:05:33.596957Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2604.24444","last_updated":"2026-04-27T13:11:57Z","snapshot_observed_at":"2026-07-06T23:10:29.508602Z","submitted_at":"2026-04-27T13:11:57Z","title":"Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-08T03:53:56.401646Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2604.24444"},"observation_digest":"sha256:019201f846a9d9ae1a1802822962c2636239a0a95a9b4d8ed2340aeb59261fed","observation_id":"eb504a6e-6396-43b7-ab80-676bdf5f0b7e","resolution":{"observed_at":"2026-05-11T21:56:11.005192Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2605.04901","last_updated":"2026-05-06T13:31:15Z","snapshot_observed_at":"2026-08-13T01:01:31.154592Z","submitted_at":"2026-05-06T13:31:15Z","title":"On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-05-08T17:24:04.123827Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2605.04901"},"observation_digest":"sha256:a8b6a93fca6571268aee41091e215c44b548d0a9ccf2407735f2a89f9afa807c","observation_id":"afd8fee2-e7a3-4b3d-9357-ddf8eada8671","resolution":{"observed_at":"2026-05-11T17:36:06.634884Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2605.14055","last_updated":"2026-05-13T19:25:56Z","snapshot_observed_at":"2026-08-11T14:34:37.134297Z","submitted_at":"2026-05-13T19:25:56Z","title":"PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-15T05:25:38.967645Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2605.14055"},"observation_digest":"sha256:d98c3f5851f456934e0ccec260e3d0361c975f7aa540b953939408087efc2ef1","observation_id":"3e71d4bb-e23e-4504-ae70-71ccab984c87","resolution":{"observed_at":"2026-05-15T05:29:48.204626Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2605.16704","last_updated":"2026-05-15T23:35:07Z","snapshot_observed_at":"2026-08-16T11:38:58.937045Z","submitted_at":"2026-05-15T23:35:07Z","title":"Convex Dataset Valuation for Post-Training","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-20T18:48:15.774746Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2605.16704"},"observation_digest":"sha256:e115dbf0a7d2923323dfd433e9fddb78c4210a7924a391b3558b380897e34afe","observation_id":"0a8f9126-3ffa-4e7f-ab2a-a67307063f6e","resolution":{"observed_at":"2026-05-20T18:48:53.219618Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2606.08814","last_updated":"2026-06-07T20:07:24Z","snapshot_observed_at":"2026-08-12T12:22:24.694247Z","submitted_at":"2026-06-07T20:07:24Z","title":"STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-27T18:28:35.162934Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2606.08814"},"observation_digest":"sha256:fcda3c94d2fef45ecca87520270f0b490b228cd9acfb4c5ddd5e46d1bcf5f043","observation_id":"8db466cf-528f-488d-aed3-16f2f09a3e90","resolution":{"observed_at":"2026-07-02T23:07:26.922351Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":"1805.12471","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-07-09T21:16:34.237758Z","title":"Neural Network Ac- ceptability Judgments","venue":"cs.CL","work_id":"ee6536e2-986a-4e85-877b-cd6cf6b9219e","year":2018},"citing_paper":{"arxiv_id":"2607.07047","last_updated":"2026-07-08T06:23:46Z","snapshot_observed_at":"2026-08-17T11:48:36.105240Z","submitted_at":"2026-07-08T06:23:46Z","title":"Riemannian Geometry for Pre-trained Language Model Embeddings","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-07-09T21:11:02.461038Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2607.07047"},"observation_digest":"sha256:bbca4efe6197b03eda693aad83774c8e9e29f85dc922c7cb1fe992dc0ec01a41","observation_id":"3a8cd40f-d486-4e07-9006-bd4e5cf6934e","resolution":{"observed_at":"2026-07-09T21:16:34.238910Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12471","snapshot_observed_at":"2026-08-01T23:15:30.865807Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15495","last_updated":"2026-07-16T22:54:30Z","snapshot_observed_at":"2026-08-19T09:54:53.193795Z","submitted_at":"2026-07-16T22:54:30Z","title":"Verbalizable Representations Form a Global Workspace in Language Models","version":1},"reference_index":172,"source":"pdf_text","source_observed_at":"2026-08-01T23:15:30.865807Z"},"links":{"cited_paper":"/paper/1805.12471","citing_paper":"/paper/2607.15495"},"observation_digest":"sha256:50e45c7ba45bc4831983038d01d6b7e193a99005fc0d638890e140738e11b5b0","observation_id":"a4d75f6e-7f1a-4681-9345-92cdfd594636","resolution":{"observed_at":"2026-08-01T23:15:30.865807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1805.12471/citation-record","integrity":"/paper/1805.12471/integrity","json":"/paper/1805.12471/citation-record.json","paper":"/paper/1805.12471"},"outbound":[],"paper":{"arxiv_id":"1805.12471","last_updated":"2019-10-01T18:41:05Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-14T19:09:01.924152Z","submitted_at":"2018-05-31T13:52:06Z","title":"Neural Network Acceptability Judgments"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 39 inbound Pith citation observations for arXiv:1805.12471."}