{"as_of":"2026-08-10T09:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6a8fc27d913a3fd8be3801e41cf250084468acaeacc4f7b222ea9e17bbc333a","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":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:59:48.929912Z","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":3,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-07T04:47:50.676984Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09643","last_updated":"2025-06-11T11:56:51Z","snapshot_observed_at":"2026-08-09T11:48:00.034085Z","submitted_at":"2025-06-11T11:56:51Z","title":"Using Sign Language Production as Data Augmentation to enhance Sign Language Translation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T04:47:50.676984Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2506.09643"},"observation_digest":"sha256:2d22fe7aeabb8debe7b6826a24c3b2af64dbac9ab8b5cfad4a0e9d310dfa7bfb","observation_id":"41537235-2c53-4a97-a0a3-da565367e547","resolution":{"observed_at":"2026-08-07T04:47:50.676984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-07T13:59:48.929912Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15690","last_updated":"2025-07-24T05:08:02Z","snapshot_observed_at":"2026-08-09T06:59:41.840938Z","submitted_at":"2025-05-26T22:10:52Z","title":"LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T13:59:48.929912Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2506.15690"},"observation_digest":"sha256:88ce6bb9da924ff916641864ca4acf019fac69ee404caca9c78bbec9875f377b","observation_id":"da71ca6e-0197-49cb-a9c3-d7a7f5be4859","resolution":{"observed_at":"2026-08-07T13:59:48.929912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-06T23:12:18.999831Z","title":"How bad is training on synthetic data? a statistical analysis of language model collapse","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19262","last_updated":"2025-06-25T03:25:04Z","snapshot_observed_at":"2026-08-10T08:47:50.887992Z","submitted_at":"2025-06-24T02:44:58Z","title":"What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:12:18.999831Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2506.19262"},"observation_digest":"sha256:c5dee77f3d81e5ff52c611392c5e5893e3056a6bbd17e6cfc46bb84523bbe8ac","observation_id":"9e77a416-a27a-4afa-88bf-749bc2797c13","resolution":{"observed_at":"2026-08-06T23:12:18.999831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-06T18:21:34.589888Z","title":"How bad is training on synthetic data? a statistical analysis of language model collapse, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08603","last_updated":"2025-07-11T13:55:45Z","snapshot_observed_at":"2026-08-09T04:59:19.398088Z","submitted_at":"2025-07-11T13:55:45Z","title":"Unlocking Speech Instruction Data Potential with Query Rewriting","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T18:21:34.589888Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2507.08603"},"observation_digest":"sha256:a62f366acc18da636a1c97a7e53d3b8a9e3de2da2c6a924921cc22af74972ecb","observation_id":"e9860435-a193-4322-89ae-96683773c6f5","resolution":{"observed_at":"2026-08-06T18:21:34.589888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-06T15:46:27.825071Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15092","last_updated":"2025-07-20T19:14:43Z","snapshot_observed_at":"2026-08-09T04:23:52.892130Z","submitted_at":"2025-07-20T19:14:43Z","title":"A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T15:46:27.825071Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2507.15092"},"observation_digest":"sha256:4a2d919b10320a5d3d8caf82fd325d2c874b4be88026849e8a330fbd31d12a24","observation_id":"de233454-e05b-4173-837b-d657cdc9e7b2","resolution":{"observed_at":"2026-08-06T15:46:27.825071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T10:38:07.338196Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03903","last_updated":"2025-09-04T05:53:58Z","snapshot_observed_at":"2026-08-10T00:17:40.251912Z","submitted_at":"2025-09-04T05:53:58Z","title":"A Generative Foundation Model for Chest Radiography","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T10:38:07.338196Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2509.03903"},"observation_digest":"sha256:19b04fd8b0f238a70bb6f149ff1ebb03988938e45c2430a4bc16b4ca00355479","observation_id":"8bd8742f-9ea8-4afa-882f-10fa73018190","resolution":{"observed_at":"2026-08-05T10:38:07.338196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T12:04:03.464968Z","title":"How bad is training on synthetic data? A statistical analysis of language model collapse,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10509","last_updated":"2025-09-02T05:46:28Z","snapshot_observed_at":"2026-08-08T07:54:22.268951Z","submitted_at":"2025-09-02T05:46:28Z","title":"The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T12:04:03.464968Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2509.10509"},"observation_digest":"sha256:7b7a33494ba129d3d326af67c82cd3df2717a2962cb6ce2510d618a45d56e252","observation_id":"4f6ba3b3-58aa-49b0-ad13-64481b3a74e9","resolution":{"observed_at":"2026-08-05T12:04:03.464968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":"2404.05090","doi":"10.48550/arxiv.2404.05090","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Bad is Training on Synthetic Data?","venue":"arXiv (Cornell University)","work_id":"483e1524-a44b-4368-b69b-9e4fd20bfabb","year":2024},"citing_paper":{"arxiv_id":"2604.21853","last_updated":"2026-04-23T16:50:00Z","snapshot_observed_at":"2026-08-06T07:33:30.847321Z","submitted_at":"2026-04-23T16:50:00Z","title":"Generative artificial intelligence reduces social welfare through model collapse","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-08T13:26:57.925635Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2604.21853"},"observation_digest":"sha256:e2acbf993a18d8e38ce992475f62e4cac836a1c33eb6bf9bad943ecfb37cfa46","observation_id":"d301647f-202c-40ee-812f-fd91324aeb54","resolution":{"observed_at":"2026-05-11T18:56:05.954915Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":"2404.05090","doi":"10.48550/arxiv.2404.05090","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Bad is Training on Synthetic Data?","venue":"arXiv (Cornell University)","work_id":"483e1524-a44b-4368-b69b-9e4fd20bfabb","year":2024},"citing_paper":{"arxiv_id":"2605.01130","last_updated":"2026-05-01T22:01:31Z","snapshot_observed_at":"2026-08-04T07:16:37.388618Z","submitted_at":"2026-05-01T22:01:31Z","title":"Iterative Finetuning is Mostly Idempotent","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-09T19:04:36.210200Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2605.01130"},"observation_digest":"sha256:c854c2fee2d5ceb1532db8fa5c8a6f041392043a434e0ab9be5ac5a953b9fac3","observation_id":"e8c76f88-3021-4b45-bfa5-d9d524545786","resolution":{"observed_at":"2026-05-11T15:51:44.493288Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":"2404.05090","doi":"10.48550/arxiv.2404.05090","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Bad is Training on Synthetic Data?","venue":"arXiv (Cornell University)","work_id":"483e1524-a44b-4368-b69b-9e4fd20bfabb","year":2024},"citing_paper":{"arxiv_id":"2605.04127","last_updated":"2026-05-29T13:50:19Z","snapshot_observed_at":"2026-07-06T23:16:54.673178Z","submitted_at":"2026-05-05T15:42:31Z","title":"Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-08T18:16:51.881163Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2605.04127"},"observation_digest":"sha256:ee881f744437a84edf1e34d30e93b789df9a3e9c92fcd9353a19dda711c68846","observation_id":"8a7e45be-ce65-4361-8a97-09e0b8041153","resolution":{"observed_at":"2026-05-09T06:35:40.856430Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":"2404.05090","doi":"10.48550/arxiv.2404.05090","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Bad is Training on Synthetic Data?","venue":"arXiv (Cornell University)","work_id":"483e1524-a44b-4368-b69b-9e4fd20bfabb","year":2024},"citing_paper":{"arxiv_id":"2605.20602","last_updated":"2026-05-20T01:44:47Z","snapshot_observed_at":"2026-07-06T23:31:08.671011Z","submitted_at":"2026-05-20T01:44:47Z","title":"Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-21T05:46:07.176408Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2605.20602"},"observation_digest":"sha256:16492d9ec270919b4141c10ff79451c6a4c41c6cf15decdd86972c3ff66aef1b","observation_id":"a1682d1f-71b2-41c1-8e89-604c1c312f94","resolution":{"observed_at":"2026-05-21T05:49:41.082711Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":"2404.05090","doi":"10.48550/arxiv.2404.05090","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Bad is Training on Synthetic Data?","venue":"arXiv (Cornell University)","work_id":"483e1524-a44b-4368-b69b-9e4fd20bfabb","year":2024},"citing_paper":{"arxiv_id":"2605.23054","last_updated":"2026-05-21T21:36:26Z","snapshot_observed_at":"2026-07-06T23:33:19.375527Z","submitted_at":"2026-05-21T21:36:26Z","title":"Model Collapse as Cultural Evolution","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-25T05:30:31.504863Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2605.23054"},"observation_digest":"sha256:a8f5ed64d0a398f269c067faed6f120ab08db110f4c2ddd5db88160453c4aac1","observation_id":"bd86a422-b354-42b2-a0d1-f1764d53e3c9","resolution":{"observed_at":"2026-05-25T05:35:23.455429Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":"2404.05090","doi":"10.48550/arxiv.2404.05090","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Bad is Training on Synthetic Data?","venue":"arXiv (Cornell University)","work_id":"483e1524-a44b-4368-b69b-9e4fd20bfabb","year":2024},"citing_paper":{"arxiv_id":"2605.28664","last_updated":"2026-05-27T15:59:45Z","snapshot_observed_at":"2026-08-07T19:59:30.749922Z","submitted_at":"2026-05-27T15:59:45Z","title":"Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T13:53:27.306664Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2605.28664"},"observation_digest":"sha256:af0e278b12d50080b8da30ece6cd3acb2a12c01ade80a009e2ee007dd6142811","observation_id":"0741d3d5-8942-4747-b3ab-676ee7135479","resolution":{"observed_at":"2026-06-29T14:03:29.933958Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse","version":1},"cited_work":{"arxiv_id":"2404.05090","doi":"10.48550/arxiv.2404.05090","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.05090","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How Bad is Training on Synthetic Data?","venue":"arXiv (Cornell University)","work_id":"483e1524-a44b-4368-b69b-9e4fd20bfabb","year":2024},"citing_paper":{"arxiv_id":"2606.28438","last_updated":"2026-06-26T07:35:43Z","snapshot_observed_at":"2026-08-03T00:04:55.169727Z","submitted_at":"2026-06-26T07:35:43Z","title":"When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs","version":1},"reference_index":151,"source":"arxiv_source","source_observed_at":"2026-06-30T01:29:42.919461Z"},"links":{"cited_paper":"/paper/2404.05090","citing_paper":"/paper/2606.28438"},"observation_digest":"sha256:24e534c40cdd2400a423d9eb092bc8875659a9ea8b170296c393d92126b6734b","observation_id":"7f84393b-0640-4913-883a-bf65095ac936","resolution":{"observed_at":"2026-06-30T01:34:09.458265Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T16:23:27.137864+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2404.05090/citation-record","integrity":"/paper/2404.05090/integrity","json":"/paper/2404.05090/citation-record.json","paper":"/paper/2404.05090"},"outbound":[],"paper":{"arxiv_id":"2404.05090","last_updated":"2024-04-07T22:15:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T21:01:04.875067Z","submitted_at":"2024-04-07T22:15:13Z","title":"How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2404.05090."}