{"as_of":"2026-08-22T15:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1322d3bad9a2439c2f2ae3ab07dee57d4da99525c8786b8b913c9c7a32cff77f","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":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":20,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:15:15.713539Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":20,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","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":"2305.17359","doi":"10.48550/arxiv.2305.17359","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.17359","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/CorpusID:263834753","venue":"arXiv (Cornell University)","work_id":"9adfc57c-cff8-42f4-8749-894f707aecc5","year":2023},"citing_paper":{"arxiv_id":"2403.07183","last_updated":"2026-05-19T03:49:32Z","snapshot_observed_at":"2026-08-01T20:53:34.992573Z","submitted_at":"2024-03-11T21:51:39Z","title":"Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T02:48:00.282814Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2403.07183"},"observation_digest":"sha256:9d32c06da52de0772972e253e2397b5ece6d530fb25fc8081a63667b4b362201","observation_id":"09f7d01d-3fca-4e01-a808-bcf90a0e56ec","resolution":{"observed_at":"2026-05-24T02:48:47.438801Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","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":"2026-08-12T18:58:01.020092Z","title":"Dna-gpt: Divergent n-gram analysis for training-free detection of gpt- generated text,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.12764","last_updated":"2024-11-17T20:13:30Z","snapshot_observed_at":"2026-08-19T09:46:16.316255Z","submitted_at":"2024-11-17T20:13:30Z","title":"SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T18:58:01.020092Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2411.12764"},"observation_digest":"sha256:0783c94ee5649b914e9464a5d243895f745c8febbbdf1a235da3e9a4fd7d36ff","observation_id":"4d0891de-71cf-4430-b879-2b421d9c64d7","resolution":{"observed_at":"2026-08-12T18:58:01.020092Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-11T20:56:41.030020Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05139","last_updated":"2025-02-09T16:59:44Z","snapshot_observed_at":"2026-08-14T11:26:41.755989Z","submitted_at":"2024-12-06T15:56:11Z","title":"A Practical Examination of AI-Generated Text Detectors for Large Language Models","version":4},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-11T20:56:41.030020Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2412.05139"},"observation_digest":"sha256:9d910cc070fc2fee732b4fa77af0c9b94af2deb2d11f5db605160794dc5ae799","observation_id":"fe7ef0f2-1608-4012-83af-91934e9c1c7c","resolution":{"observed_at":"2026-08-11T20:56:41.030020Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-16T21:35:09.184757Z","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:a41b4363d273b5ca0cbe4d52e1ef132e2365a6f05d2fc4f7d8e0838567b31430","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":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","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":"2026-08-11T14:56:07.786429Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11517","last_updated":"2025-02-04T10:52:02Z","snapshot_observed_at":"2026-08-19T03:07:51.061625Z","submitted_at":"2024-12-16T07:51:09Z","title":"DART: An AIGT Detector using AMR of Rephrased Text","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T14:56:07.786429Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2412.11517"},"observation_digest":"sha256:37669950e0e885038c3eaeecf56e804da547337223d9d48e46118f0de01377ea","observation_id":"fc70f81c-d366-4b55-baa8-ece71991559f","resolution":{"observed_at":"2026-08-11T14:56:07.786429Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-10T18:48:02.497063Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.11012","last_updated":"2025-02-22T12:49:12Z","snapshot_observed_at":"2026-08-14T21:20:32.782460Z","submitted_at":"2025-01-19T11:11:55Z","title":"GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-10T18:48:02.497063Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2501.11012"},"observation_digest":"sha256:98f283f282eb47f1a23c0124ac9b3101fa82192cfd2cbccca64b058b03a5b236","observation_id":"b7e9e02c-fcee-4aaa-bc1c-59a58aa3e314","resolution":{"observed_at":"2026-08-10T18:48:02.497063Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-09T21:45:25.842666Z","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":"2501.18998","last_updated":"2025-04-10T18:46:55Z","snapshot_observed_at":"2026-08-13T16:57:07.591579Z","submitted_at":"2025-01-31T10:06:27Z","title":"Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T21:45:25.842666Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2501.18998"},"observation_digest":"sha256:bb5cadbe541248f795eda3775c43f1a041d552107b0f957de92227730eb13661","observation_id":"146cfae7-077b-4eee-8851-e043b9115125","resolution":{"observed_at":"2026-08-09T21:45:25.842666Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-08T21:12:22.522274Z","title":"Dna-gpt: Divergent n-gram analysis for training-free detection of gpt-generated text","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05240","last_updated":"2025-02-12T14:43:02Z","snapshot_observed_at":"2026-08-19T16:33:48.605529Z","submitted_at":"2025-02-07T12:18:20Z","title":"Survey on AI-Generated Media Detection: From Non-MLLM to MLLM","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T21:12:22.522274Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2502.05240"},"observation_digest":"sha256:c8f5b793641fba2456ea9e8e872f2d3d0f89976f8c5f8db3581542af9712b69c","observation_id":"1425f8bd-a6bc-4377-9358-8925b655f665","resolution":{"observed_at":"2026-08-08T21:12:22.522274Z","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":"2026-08-16T15:29:06.598244Z","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":"2305.17359","doi":"10.48550/arxiv.2305.17359","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.17359","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/CorpusID:263834753","venue":"arXiv (Cornell University)","work_id":"9adfc57c-cff8-42f4-8749-894f707aecc5","year":2023},"citing_paper":{"arxiv_id":"2502.11336","last_updated":"2026-05-05T14:15:33Z","snapshot_observed_at":"2026-08-16T15:59:27.805712Z","submitted_at":"2025-02-17T01:15:07Z","title":"ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-23T03:27:02.516990Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2502.11336"},"observation_digest":"sha256:08e91e6af41a14ebe042fcd8550ff2a9b7fbd4a036992a5a2e8825ba6f649662","observation_id":"d02abb10-a263-4004-a24a-61e2b73e67fa","resolution":{"observed_at":"2026-05-23T03:27:26.959705Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","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":"2026-08-07T15:37:16.194370Z","title":"12 A BREAKDOWN OFPERFORMANCE BYMETHOD, DATASET,ANDDETECTOR In this section, we break down the performance of all methods, evaluated on all datasets and detectors","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14608","last_updated":"2026-06-08T19:42:11Z","snapshot_observed_at":"2026-08-21T20:26:13.276475Z","submitted_at":"2025-05-20T16:55:44Z","title":"Attacks on Machine-Text Detectors Retain Stylistic Fingerprints","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:37:16.194370Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2505.14608"},"observation_digest":"sha256:9c2484a1441e7e5b6422776e95ff6730f06c5d867dd5fcf8596d4aee6c06499c","observation_id":"bc1cd6cc-f29c-40d5-b7c6-80f01b329a4b","resolution":{"observed_at":"2026-08-07T15:37:16.194370Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-15T19:15:15.713539Z","title":"DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text.ArXiv, abs/2305.17359, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.17467","last_updated":"2026-07-31T16:38:52Z","snapshot_observed_at":"2026-08-19T18:08:05.085010Z","submitted_at":"2025-06-20T20:15:09Z","title":"Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems","version":2},"reference_index":215,"source":"pdf_text","source_observed_at":"2026-08-15T19:15:15.713539Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2506.17467"},"observation_digest":"sha256:e71ab9cb0f5ecc171e39bfaeda4960b01b892f3d40fd9d0519dc9932c52a157d","observation_id":"e338870c-f947-4c42-8456-fa817ccb206d","resolution":{"observed_at":"2026-08-15T19:15:15.713539Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-06T20:06:18.882765Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.03887","last_updated":"2025-07-05T03:59:17Z","snapshot_observed_at":"2026-08-21T16:34:08.673603Z","submitted_at":"2025-07-05T03:59:17Z","title":"Traceable TTS: Toward Watermark-Free TTS with Strong Traceability","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T20:06:18.882765Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2507.03887"},"observation_digest":"sha256:3e905b5810fc342e3323acc8cc9df3b738c05bb57a0c5f46e0c9e4fbf8ddea6f","observation_id":"a769934f-57f5-46ad-afcd-7d17b06314ca","resolution":{"observed_at":"2026-08-06T20:06:18.882765Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-06T16:34:09.380391Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13123","last_updated":"2025-07-17T13:38:16Z","snapshot_observed_at":"2026-08-16T17:49:46.580843Z","submitted_at":"2025-07-17T13:38:16Z","title":"Detecting LLM-generated Code with Subtle Modification by Adversarial Training","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T16:34:09.380391Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2507.13123"},"observation_digest":"sha256:42adc8e4cae268e1117a9b40dfbe3b9c8434d66398dc73de4804e4376bb8c16f","observation_id":"d3ff042d-b90f-4b45-a693-982183eb2a68","resolution":{"observed_at":"2026-08-06T16:34:09.380391Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-06T15:53:17.633747Z","title":"Dna-gpt: Divergent n-gram analysis for training-free detection of gpt-generated text","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.14805","last_updated":"2025-07-20T03:51:13Z","snapshot_observed_at":"2026-08-14T23:23:40.747426Z","submitted_at":"2025-07-20T03:51:13Z","title":"Subliminal Learning: Language models transmit behavioral traits via hidden signals in data","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T15:53:17.633747Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2507.14805"},"observation_digest":"sha256:c2d5145382862a8b010ab62629443093577894d1494144b47249850b676da51c","observation_id":"c79ad620-890e-4284-bf4a-edf970420b82","resolution":{"observed_at":"2026-08-06T15:53:17.633747Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-06T15:42:53.763451Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.15286","last_updated":"2025-07-21T06:37:27Z","snapshot_observed_at":"2026-08-21T11:18:38.262889Z","submitted_at":"2025-07-21T06:37:27Z","title":"Beyond Easy Wins: A Text Hardness-Aware Benchmark for LLM-generated Text Detection","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-06T15:42:53.763451Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2507.15286"},"observation_digest":"sha256:2259a2485b0420b5fb15efb4693dd5384a29f71f696a3b418aa09d25dc407e0e","observation_id":"14e63582-4d1c-4dc6-9f15-310bdbc0c172","resolution":{"observed_at":"2026-08-06T15:42:53.763451Z","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":"2026-08-16T15:29:06.598244Z","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":"2026-08-04T11:01:44.330521Z","title":"Dna-gpt: Divergent n-gram analysis for training-free detection of gpt-generated text, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.07500","last_updated":"2026-06-22T09:17:40Z","snapshot_observed_at":"2026-08-16T12:07:27.160334Z","submitted_at":"2025-10-08T19:53:11Z","title":"Black-Box Detection of LLM-Generated Text Using Generalized Jensen-Shannon Divergence","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-04T11:01:44.330521Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2510.07500"},"observation_digest":"sha256:57746c2632240c4bd3c30389c2d9d49d120c76a6b270d22c016a32537d6046ab","observation_id":"fe95b911-010c-4527-a03e-e46b9c39a25d","resolution":{"observed_at":"2026-08-04T11:01:44.330521Z","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":"2026-08-16T15:29:06.598244Z","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":"2305.17359","doi":"10.48550/arxiv.2305.17359","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.17359","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/CorpusID:263834753","venue":"arXiv (Cornell University)","work_id":"9adfc57c-cff8-42f4-8749-894f707aecc5","year":2023},"citing_paper":{"arxiv_id":"2604.26328","last_updated":"2026-04-29T06:22:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-29T06:22:08Z","title":"DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-07T13:13:35.419106Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2604.26328"},"observation_digest":"sha256:1bf51bb334c959a0f0f26a7e100bc7cfc287a30cd60ce95b3ed37e781e9222d6","observation_id":"a7ce7b26-d54c-41ab-b1df-1e117178e4bb","resolution":{"observed_at":"2026-05-12T09:01:26.031663Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","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":"2305.17359","doi":"10.48550/arxiv.2305.17359","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.17359","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/CorpusID:263834753","venue":"arXiv (Cornell University)","work_id":"9adfc57c-cff8-42f4-8749-894f707aecc5","year":2023},"citing_paper":{"arxiv_id":"2605.20761","last_updated":"2026-05-25T09:02:56Z","snapshot_observed_at":"2026-08-17T23:11:49.924354Z","submitted_at":"2026-05-20T06:01:17Z","title":"Findings of the Counter Turing Test: AI-Generated Text Detection","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T17:32:43.664618Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2605.20761"},"observation_digest":"sha256:2ec708894f3814cfaa6c97cea1bc58cbd2b947801a8717981de647497296f085","observation_id":"f08c60ea-dc3e-4bb6-bd78-ee2281fbfc83","resolution":{"observed_at":"2026-06-30T17:34:57.506353Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","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":"2305.17359","doi":"10.48550/arxiv.2305.17359","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.17359","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/CorpusID:263834753","venue":"arXiv (Cornell University)","work_id":"9adfc57c-cff8-42f4-8749-894f707aecc5","year":2023},"citing_paper":{"arxiv_id":"2606.04205","last_updated":"2026-06-02T20:49:20Z","snapshot_observed_at":"2026-08-15T07:29:39.967927Z","submitted_at":"2026-06-02T20:49:20Z","title":"DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T07:07:33.410608Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2606.04205"},"observation_digest":"sha256:594cb95fa9fdf7ac01b55581da9287fca0588bc7b5d683f1040e20f88417847e","observation_id":"66b95946-658a-40cd-bd1d-adbf5fbc146a","resolution":{"observed_at":"2026-07-02T07:06:44.550724Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","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":"2305.17359","doi":"10.48550/arxiv.2305.17359","metadata_source":"arxiv_reference","pith_arxiv_id":"2305.17359","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"org/CorpusID:263834753","venue":"arXiv (Cornell University)","work_id":"9adfc57c-cff8-42f4-8749-894f707aecc5","year":2023},"citing_paper":{"arxiv_id":"2606.07313","last_updated":"2026-06-05T14:34:37Z","snapshot_observed_at":"2026-08-14T11:47:58.541040Z","submitted_at":"2026-06-05T14:34:37Z","title":"SV-Detect: AI-generated Text Detection with Steering Vectors","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-06-27T22:16:31.088030Z"},"links":{"cited_paper":"/paper/2305.17359","citing_paper":"/paper/2606.07313"},"observation_digest":"sha256:65847f052531fa0d36b1a155ac6fcb7dbb078d6b090ab4fe4f71411fb1d74664","observation_id":"dc520eb3-73f8-4d0d-a28a-c221c1217a78","resolution":{"observed_at":"2026-06-27T22:21:21.120909Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.17359/citation-record","integrity":"/paper/2305.17359/integrity","json":"/paper/2305.17359/citation-record.json","paper":"/paper/2305.17359"},"outbound":[],"paper":{"arxiv_id":"2305.17359","last_updated":"2023-10-04T16:36:09Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T15:29:06.598244Z","submitted_at":"2023-05-27T03:58:29Z","title":"DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2305.17359."}