{"as_of":"2026-08-12T16:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:62ecb0c60c985b2e635ad610284b7eddcbf99a5e77c4816b3f20a16fc5b0389f","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:56:47.966630Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2412.11506/citation-record","integrity":"/paper/2412.11506/integrity","json":"/paper/2412.11506/citation-record.json","paper":"/paper/2412.11506"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.10403","last_updated":"2023-09-13T20:35:45Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T17:46:53Z","title":"PaLM 2 Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10403","snapshot_observed_at":"2026-08-11T14:56:47.768166Z","title":"Palm 2 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.768166Z"},"links":{"cited_paper":"/paper/2305.10403","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:5ad2fa7c5a06dc5d0b768c0b3e0c3c40204a531fe741162d123a57123aab41f1","observation_id":"f860f01f-a897-4745-b645-93e04c8b8904","resolution":{"observed_at":"2026-08-11T14:56:47.768166Z","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":"2026-08-11T14:56:48.435495Z","title":"Hierarchical neural story generation","venue":null,"work_id":"60e4fcac-bc14-4d0e-bcb3-978ef55f8367","year":2025},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.797504Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:43e2ff282d03cc466937f63c5b38a45754610cf2998cf49d864b674d596e324a","observation_id":"88718a1e-256a-49bd-9152-f72139e322d9","resolution":{"observed_at":"2026-08-11T14:56:48.440238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.02792","last_updated":"2019-04-04T21:03:34Z","snapshot_observed_at":"2026-08-05T03:39:53.988683Z","submitted_at":"2019-04-04T21:03:34Z","title":"Unifying Human and Statistical Evaluation for Natural Language Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.02792","snapshot_observed_at":"2026-08-11T14:56:47.803073Z","title":"Unifying human and statistical evaluation for natural language generation","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.803073Z"},"links":{"cited_paper":"/paper/1904.02792","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:d877af5c9c0b74510df99c4baf9f2dd120385bbad83fef669d11a31a8cedbc79","observation_id":"01dd2b39-8eb4-41da-b6f0-1bbb73b79f8b","resolution":{"observed_at":"2026-08-11T14:56:47.803073Z","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":"2026-08-11T14:56:47.809862Z","title":"Pubmedqa: A dataset for biomedical research question answering","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.809862Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:a8e27cd165d463aa12f6555e154df5370d559a92493bd47289c9eaa48e54941b","observation_id":"ca4370ce-ca12-4ba8-8353-3c598d65e8ea","resolution":{"observed_at":"2026-08-11T14:56:47.809862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15593","last_updated":"2024-06-06T04:53:25Z","snapshot_observed_at":"2026-07-06T15:59:52.247013Z","submitted_at":"2023-07-28T14:52:08Z","title":"Robust Distortion-free Watermarks for Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15593","snapshot_observed_at":"2026-08-11T14:56:47.833459Z","title":"Robust distortion-free watermarks for language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.833459Z"},"links":{"cited_paper":"/paper/2307.15593","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:b983fe6488e60b67c05117b7e8f76560fcc07688a1543e3be147d94898d3cb2d","observation_id":"5e7444d2-7d91-4f76-a2a5-07fc9a5feecc","resolution":{"observed_at":"2026-08-11T14:56:47.833459Z","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":"2026-08-11T14:56:48.412039Z","title":"Ai- generated text boundary detection with roft","venue":null,"work_id":"61a291af-d4f4-47b7-a4e6-edbb2eace5a9","year":2024},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.840443Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:1248834e56523b3b3dbdb4dcc38e7bce9bd37385f798676079fa0baa72bbf7d5","observation_id":"550cb805-a2c5-4e48-b7e8-d9735e6383fe","resolution":{"observed_at":"2026-08-11T14:56:48.417101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T14:56:48.396581Z","title":"Detecting fake content with relative en- tropy scoring","venue":null,"work_id":"214db4eb-5252-45e7-88e7-2f2ee24216c8","year":2008},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.846287Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:ee5bf5d0dd63ab43648b3ff286e4bbb7585e6377a03d2f2057de97d3313bb2c1","observation_id":"ef68aa74-bebb-4e06-8067-046afc2c3448","resolution":{"observed_at":"2026-08-11T14:56:48.400695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.09859","last_updated":"2024-02-24T19:47:14Z","snapshot_observed_at":"2026-08-12T09:55:12.930582Z","submitted_at":"2023-05-17T00:09:08Z","title":"Smaller Language Models are Better Black-box Machine-Generated Text Detectors","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.09859","snapshot_observed_at":"2026-08-11T14:56:47.851062Z","title":"Smaller language models are better black-box machine-generated text detectors","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.851062Z"},"links":{"cited_paper":"/paper/2305.09859","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:71ccba8b9e1aec9cb89cdec648f1b17739feafdf9f1a6bc992dd48efd2a3b0d8","observation_id":"0fa76088-0f1c-404a-869b-43e6656dbde7","resolution":{"observed_at":"2026-08-11T14:56:47.851062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.11305","last_updated":"2023-07-23T04:18:36Z","snapshot_observed_at":"2026-08-09T14:25:45.199565Z","submitted_at":"2023-01-26T18:44:06Z","title":"DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.11305","snapshot_observed_at":"2026-08-11T14:56:47.856656Z","title":"De- tectgpt: Zero-shot machine-generated text detection using probability curvature","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.856656Z"},"links":{"cited_paper":"/paper/2301.11305","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:9dcbda9e2154883c16f5e8fe029eeb8e7ee7db7d16d8298c3a88def14d8f57a6","observation_id":"f6682fc7-2574-42aa-a49b-8adf74d744d2","resolution":{"observed_at":"2026-08-11T14:56:47.856656Z","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":"2026-08-11T14:56:47.861928Z","title":"Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.861928Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:b8ca15ee10344e0eb2f1ec26dc537713da7a2824d38377696f0a51fcbc9ff59e","observation_id":"9e1ea547-ff6b-4bc3-81fd-4bd24d77423c","resolution":{"observed_at":"2026-08-11T14:56:47.861928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-11T14:56:47.867472Z","title":"GPT-4 Technical Report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.867472Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:0859e5b1bde0ab93a12ab1404cbc9a1afb4adff1bac11ef89b791884a116850a","observation_id":"52b13bbf-4755-4994-9b0f-33ff15ffc8c9","resolution":{"observed_at":"2026-08-11T14:56:47.867472Z","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":"2026-08-11T14:56:48.375773Z","title":"On the risk of misinformation pollution with large language models","venue":null,"work_id":"c7b31111-df8f-4697-bf65-02386d84a0f3","year":2023},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.873857Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:a76e6eaf037749c9b44b926638ed6d5598c9c32ddd15db49421e83af6d777ef9","observation_id":"ae2c7f09-e596-42d0-be99-93cbf4cbae8a","resolution":{"observed_at":"2026-08-11T14:56:48.380622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.09203","last_updated":"2019-11-13T03:54:12Z","snapshot_observed_at":"2026-08-03T18:37:05.537362Z","submitted_at":"2019-08-24T20:41:40Z","title":"Release Strategies and the Social Impacts of Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.09203","snapshot_observed_at":"2026-08-11T14:56:47.882886Z","title":"Release strategies and the social impacts of language models","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.882886Z"},"links":{"cited_paper":"/paper/1908.09203","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:61ccc97aacbe80a4ab312b77cd68d7ca9021dfd3bbaf76b06c2662ffea5515d4","observation_id":"ecfcfbfa-4256-48c5-896a-afef16b20412","resolution":{"observed_at":"2026-08-11T14:56:47.882886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05561","last_updated":"2024-09-30T10:17:12Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-01-10T22:07:21Z","title":"TrustLLM: Trustworthiness in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05561","snapshot_observed_at":"2026-08-11T14:56:47.900533Z","title":"Trustllm: Trustworthiness in large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.900533Z"},"links":{"cited_paper":"/paper/2401.05561","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:6e6e758b143ed0113089fdee5e8d41eef75b77f028cff8c6536b3a5c9bdb7f4d","observation_id":"8a75f85a-a85c-40b0-a103-d0716cd690d4","resolution":{"observed_at":"2026-08-11T14:56:47.900533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.20127","last_updated":"2024-03-29T11:33:34Z","snapshot_observed_at":"2026-08-12T14:04:03.190462Z","submitted_at":"2024-03-29T11:33:34Z","title":"The Impact of Prompts on Zero-Shot Detection of AI-Generated Text","version":1},"cited_work":{"arxiv_id":"2403.20127","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.20127","snapshot_observed_at":"2026-08-11T14:56:48.118243Z","title":"The Impact of Prompts on Zero-Shot Detection of AI-Generated Text","venue":"cs.AI","work_id":"c4ad93d0-4ab8-483e-b6d6-bc6fee38a171","year":2024},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.907276Z"},"links":{"cited_paper":"/paper/2403.20127","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:b9f0f62b0d399878fe7cdddf01e739ebc71a6037e09259d766e8d32d31cc1295","observation_id":"2ba095a7-3fb1-4600-b899-9ffd2a4035a7","resolution":{"observed_at":"2026-08-11T14:56:48.122876Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-11T14:56:47.912104Z","title":"Gemini: a family of highly capable multimodal models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.912104Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:707b9299ead4f003a9601180307f5f1aa0092f6d2e914a6e3022a5b930b61d4b","observation_id":"4b7c22c4-3801-4e59-a27b-7daf0606b50b","resolution":{"observed_at":"2026-08-11T14:56:47.912104Z","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":"2026-08-11T14:56:48.360582Z","title":"Authorship attribution for neural text genera- tion","venue":null,"work_id":"a30a8127-af5a-4474-9b72-e829dd0b85e3","year":2020},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.918360Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:8b579aec6982341b3d2e3cdcad347b2c2d6321560588ec875c58b6c975455a8d","observation_id":"e96d5602-cc98-4c23-8c17-fdab17123694","resolution":{"observed_at":"2026-08-11T14:56:48.364652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.04359","last_updated":"2021-12-08T16:09:48Z","snapshot_observed_at":"2026-08-09T15:17:43.394064Z","submitted_at":"2021-12-08T16:09:48Z","title":"Ethical and social risks of harm from Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.04359","snapshot_observed_at":"2026-08-11T14:56:47.927365Z","title":"Ethical and social risks of harm from language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.927365Z"},"links":{"cited_paper":"/paper/2112.04359","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:4c712083822fc5eb8676b84656fed90f87031efd55aa3607aeaec8b6a707ce48","observation_id":"59a5c25a-c31d-4274-b6de-55ccf7ca845a","resolution":{"observed_at":"2026-08-11T14:56:47.927365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.19874","last_updated":"2024-10-09T09:36:49Z","snapshot_observed_at":"2026-08-04T00:50:55.687283Z","submitted_at":"2024-06-28T12:28:52Z","title":"Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood","version":2},"cited_work":{"arxiv_id":"2406.19874","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.19874","snapshot_observed_at":"2026-08-11T14:56:48.060499Z","title":"Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood","venue":"cs.CL","work_id":"ac3598c6-279c-4cbb-9969-80a55cab46fa","year":2024},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.933115Z"},"links":{"cited_paper":"/paper/2406.19874","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:4f9131cd714377b5869cd08080075fbd4ca7d7bd6adc09b0659d3c56a84a1e5e","observation_id":"2655fd17-3758-456f-937a-fc1b2f03bdc6","resolution":{"observed_at":"2026-08-11T14:56:48.065771Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-08-11T14:56:47.938333Z","title":"Qwen2 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.938333Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:3db1f19a912ed19e4f12b08f7a96269e62f5d9436934964f313cf818cdb3aa22","observation_id":"f5b53b18-a8ec-49d8-bea0-545610436e8f","resolution":{"observed_at":"2026-08-11T14:56:47.938333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-06T09:46:48.055328Z","submitted_at":"2023-05-27T03:58:29Z","title":"DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.17359","snapshot_observed_at":"2026-08-11T14:56:47.945503Z","title":"Dna-gpt: Divergent n-gram analysis for training-free detection of gpt-generated text","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.945503Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:a521098df089b56bd5938d4811e9de2a08e6b6b471b5b66cd27419c046b88978","observation_id":"eb5ced82-6650-43aa-88c3-061654ac8624","resolution":{"observed_at":"2026-08-11T14:56:47.945503Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05232","last_updated":"2024-10-27T09:55:40Z","snapshot_observed_at":"2026-08-10T09:55:44.151092Z","submitted_at":"2024-06-07T19:38:05Z","title":"DALD: Improving Logits-based Detector without Logits from Black-box LLMs","version":4},"cited_work":{"arxiv_id":"2406.05232","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.05232","snapshot_observed_at":"2026-08-11T14:56:48.005739Z","title":"DALD: Improving Logits-based Detector without Logits from Black-box LLMs","venue":"cs.CL","work_id":"9b84581e-fad1-440d-af8d-8fcd0c366b89","year":2024},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.951831Z"},"links":{"cited_paper":"/paper/2406.05232","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:ff770dacfbe91706b0b814dbfb104105b8047adef69a2ca36519666eed4290a2","observation_id":"783c269c-fe71-406f-a294-acf6307ef308","resolution":{"observed_at":"2026-08-11T14:56:48.014011Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T14:56:48.321270Z","title":"The ranges are empirically decided, which balance the coverage of the possible choices and the size of the table","venue":null,"work_id":"4de891db-1bbf-4eb0-92a2-6c6b4f8e6dee","year":2025},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.962195Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:0235fe33c1f937c8e870b0a9415b30e528d8940fbd9eddfea597c21b8b80510b","observation_id":"06bad759-38a6-41e4-878a-a987ae0f20e3","resolution":{"observed_at":"2026-08-11T14:56:48.325151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T14:56:48.308658Z","title":"Our testing encompasses two paraphrasing settings: high lexical diversity (60 L) and high-order diversity (60 O)","venue":null,"work_id":"e9b3eeb0-7a07-4555-8f31-4a4fe9d960e6","year":2025},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.966630Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:873b9f4a3eba48ecc6736da40dcae281117cecdac057bf5d055e17e90e65b739","observation_id":"d2f6884b-fd14-4182-94fd-3adb8b94f0a9","resolution":{"observed_at":"2026-08-11T14:56:48.312888Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T14:56:48.334212Z","title":"We approximate the pattern using parameterized distributions, allocating the remaining probability mass (as ‘*’ indicates) to ranks larger thanK","venue":null,"work_id":"0ddb19db-2576-45cd-91cc-7ec0022d1fca","year":2025},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.957590Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:feb7bb5f19773dc3168eaed96148e05f33d6a6c58fcf5ead963dcb90d3b6a624","observation_id":"a4d6ff45-4c73-4ba0-bbb9-7d3596146caa","resolution":{"observed_at":"2026-08-11T14:56:48.338384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11156","last_updated":"2025-01-17T04:21:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-17T17:53:19Z","title":"Can AI-Generated Text be Reliably Detected?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.11156","snapshot_observed_at":"2026-08-11T14:56:47.878195Z","title":"Can ai-generated text be reliably detected? arXiv preprint arXiv:2303.11156,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.878195Z"},"links":{"cited_paper":"/paper/2303.11156","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:002b2cfb1dcd1e084aa3e78f00d3080ac600335e48d369b216e86541399d1eb5","observation_id":"e3ad9a68-210b-4e35-a73f-f33ccfa7ff81","resolution":{"observed_at":"2026-08-11T14:56:47.878195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05540","last_updated":"2023-05-23T11:18:30Z","snapshot_observed_at":"2026-08-12T14:46:34.504813Z","submitted_at":"2023-05-23T11:18:30Z","title":"DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05540","snapshot_observed_at":"2026-08-11T14:56:47.888641Z","title":"Detectllm: Leveraging log rank informa- tion for zero-shot detection of machine-generated text","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.888641Z"},"links":{"cited_paper":"/paper/2306.05540","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:4e7838adacbf0b3f16961bcec9123902fbcb5b7a27e13e2f418c98be8dee65e5","observation_id":"6dfb1344-8f2d-4a69-a920-eef6957c84c8","resolution":{"observed_at":"2026-08-11T14:56:47.888641Z","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":"2026-08-11T14:56:48.346753Z","title":"Ghostbuster: Detecting text ghostwrit- ten by large language models","venue":null,"work_id":"79966807-9d48-45d3-8c77-18d37b2de7c5","year":2025},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.922871Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:5a1b15874db8ac4a844de1174aa41e0da159de5c57dfb514d0f3bc3e8ec98596","observation_id":"6f219c1e-1a1e-4b0e-87b4-300c85b5e49c","resolution":{"observed_at":"2026-08-11T14:56:48.351731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T14:56:47.784500Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.784500Z"},"links":{"citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:b33a94ac3ede81697ae69f444c3a18da6544917ef209c93d8c5c1709621d5a13","observation_id":"899ae7ee-2a3c-458c-98e7-a8c59258c72a","resolution":{"observed_at":"2026-08-11T14:56:47.784500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10226","last_updated":"2024-05-01T22:04:31Z","snapshot_observed_at":"2026-07-06T14:44:16.786216Z","submitted_at":"2023-01-24T18:52:59Z","title":"A Watermark for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.10226","snapshot_observed_at":"2026-08-11T14:56:47.816126Z","title":"A watermark for large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.816126Z"},"links":{"cited_paper":"/paper/2301.10226","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:f4a55f006548900b4089c2bd9b96a78dc343f93dac0b1f194fe0abdd1c2827f8","observation_id":"bdae5f9c-5928-44e3-837b-96632a3717ad","resolution":{"observed_at":"2026-08-11T14:56:47.816126Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-10T16:40:37.411115Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T14:56:47.790027Z","title":"The llama 3 herd of models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.790027Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:ba675ccd5e745669b6ae0b7662dab8e157f6e5227c77a12628b06f71d095c406","observation_id":"fb6a51e4-2f37-4574-8231-f6c49574c396","resolution":{"observed_at":"2026-08-11T14:56:47.790027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.03351","last_updated":"2019-11-25T16:21:00Z","snapshot_observed_at":"2026-08-09T09:47:34.637204Z","submitted_at":"2019-06-07T22:45:33Z","title":"Real or Fake? Learning to Discriminate Machine from Human Generated Text","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.03351","snapshot_observed_at":"2026-08-11T14:56:47.773233Z","title":"Real or fake? learning to discriminate machine from human generated text","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T14:56:47.773233Z"},"links":{"cited_paper":"/paper/1906.03351","citing_paper":"/paper/2412.11506"},"observation_digest":"sha256:49242b57c0d66d951963d67811032bc013954184f6e0ddf8220fc5532e9777d4","observation_id":"8af67723-e65a-412d-b12b-a2439a2b5ded","resolution":{"observed_at":"2026-08-11T14:56:47.773233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.11506","last_updated":"2025-02-19T07:37:55Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-12T11:08:59.823486Z","submitted_at":"2024-12-16T07:28:36Z","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":3,"verified_fuzzy":8},"total_outbound_references":32},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2412.11506."}