{"as_of":"2026-08-15T20:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2bb1cf39783a368617cf0711a53b6dee3974691953acc8d00063fc08495a2274","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:43:30.905284Z","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":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T23:43:05.423799Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.18055","snapshot_observed_at":"2026-08-01T23:43:05.423799Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15277","last_updated":"2026-07-16T17:59:31Z","snapshot_observed_at":"2026-08-13T02:49:08.522823Z","submitted_at":"2026-07-16T17:59:31Z","title":"Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T23:43:05.423799Z"},"links":{"cited_paper":"/paper/2507.18055","citing_paper":"/paper/2607.15277"},"observation_digest":"sha256:4433a2972e44a1cd03ec11de3064d1672544a768930392841362a9509df1d70e","observation_id":"33449d48-ee6b-4199-b1c5-e86cb48708a2","resolution":{"observed_at":"2026-08-01T23:43:05.423799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.18055/citation-record","integrity":"/paper/2507.18055/integrity","json":"/paper/2507.18055/citation-record.json","paper":"/paper/2507.18055"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.04190","last_updated":"2024-03-07T03:38:44Z","snapshot_observed_at":"2026-08-14T07:06:19.096827Z","submitted_at":"2024-03-07T03:38:44Z","title":"Generative AI for Synthetic Data Generation: Methods, Challenges and the Future","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04190","snapshot_observed_at":"2026-08-06T14:43:28.885311Z","title":"Generative ai for synthetic data generation: Methods, challenges and the future.arXiv preprint arXiv:2403.04190, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:28.885311Z"},"links":{"cited_paper":"/paper/2403.04190","citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:3d790ee5590717ca68a76c1fa544f2a32864bbfdee70037c9189aa8545c1f3a5","observation_id":"07fe07c5-6d8d-4ad3-9ed7-9a82238dc1dd","resolution":{"observed_at":"2026-08-06T14:43:28.885311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.01629","last_updated":"2024-01-03T09:03:30Z","snapshot_observed_at":"2026-08-13T04:49:42.370105Z","submitted_at":"2024-01-03T09:03:30Z","title":"Synthetic Data in AI: Challenges, Applications, and Ethical Implications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.01629","snapshot_observed_at":"2026-08-06T14:43:28.979022Z","title":"Synthetic data in ai: Challenges, applications, and ethical implications.arXiv preprint arXiv:2401.01629, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:28.979022Z"},"links":{"cited_paper":"/paper/2401.01629","citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:d2b7736ffaf9d4dcea7fbb45bdc811a7f810c721c7926acce8587d7a3de53eb1","observation_id":"c1f52099-b92f-47b6-9d07-143fdc29dfaa","resolution":{"observed_at":"2026-08-06T14:43:28.979022Z","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":"2026-08-06T14:43:29.064360Z","title":"Extracting training data from large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.064360Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:b6257b09a439bfd304b4912223f4f4c41c3f57cb16fe907ea4f058332d1af6d6","observation_id":"3bf9f5f8-1532-458e-b6cd-e8e137588b1a","resolution":{"observed_at":"2026-08-06T14:43:29.064360Z","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":"2026-08-06T14:43:29.202971Z","title":"Security and privacy challenges of large language models: A survey.ACM Computing Surveys, 57(6):1–39, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.202971Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:41f6a96c852e17d5e4cf218d661aed17f0998621f3e2f247d832312558f88b0c","observation_id":"c3da9d12-ace7-4510-a590-9919dcbd39d7","resolution":{"observed_at":"2026-08-06T14:43:29.202971Z","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":"2026-08-06T14:43:33.523067Z","title":"Evaluating large language models in generating synthetic hci research data: a case study","venue":null,"work_id":"31cc7639-a3be-463d-8cd1-c90f6b774fd0","year":2023},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.305712Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:7a7c10bb2818ceb2aa5c144ce4b4ce94c252a274f1fa86a0b187623540a45cae","observation_id":"2820e3f1-a4ea-489b-bc0f-86783df570f0","resolution":{"observed_at":"2026-08-06T14:43:33.656799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:33.370760Z","title":null,"venue":null,"work_id":"aadad1c5-e581-4fba-92b8-f3c3260e7bdb","year":2024},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.412622Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:ebf5b8869a5a81a2e298af5698b2568876f31fcc9447f044fcd25059a8d0e4ba","observation_id":"e7cc00ab-b9ee-46c9-a5fb-52b4d7868f32","resolution":{"observed_at":"2026-08-06T14:43:33.445971Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:33.183677Z","title":"Siu, Byron C","venue":null,"work_id":"3acfd8a9-f1c7-4602-815f-fa6f81438930","year":2025},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.546725Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:d53775cc4f4f406e5657ad9a4a666670ac1c19e8fc4c37a76540c8939794c921","observation_id":"e8dc92ce-77ef-46a8-934a-37309d503710","resolution":{"observed_at":"2026-08-06T14:43:33.288280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:33.028120Z","title":"Neural review rating prediction with user and product memory","venue":null,"work_id":"237160be-1ff0-4a9c-9701-0665a5921995","year":2019},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.653053Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:162ca95be942d9d7cdb4c0c8da61c0379a3792fafde96c7228cd0cfa08f92f71","observation_id":"b54b1180-67fc-43aa-8a3a-b9da7ac3e24a","resolution":{"observed_at":"2026-08-06T14:43:33.111569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:29.795320Z","title":"Jointly measuring diversity and quality in text generation models, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.795320Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:4cf4d06884f9cb259fb75d8fb4266559a5084e02b7988b05635cc82928ba5745","observation_id":"b18bae4f-53a2-4eb1-b9f7-0bac598d4124","resolution":{"observed_at":"2026-08-06T14:43:29.795320Z","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":"2026-08-06T14:43:32.844129Z","title":"Lautrup, Tobias Hyrup, Arthur Zimek, and Peter Schneider-Kamp","venue":null,"work_id":"0b1a86ca-5d83-4a86-904a-acef1254ba69","year":2024},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:29.926384Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:8faf18b3b703313062caa91600615517cea703347aacdd2fbc5ffbf42fe6f5b2","observation_id":"9fcf71af-0a5c-4d54-b847-7aa98ca8560c","resolution":{"observed_at":"2026-08-06T14:43:32.948413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:32.666218Z","title":"Della Vedova, Daniele Tessera, Daniele Toti, and Nicola Vanoli","venue":null,"work_id":"e1e9d85e-3b43-4c63-80c0-d362377a46ba","year":2022},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.054165Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:c6c0d8227d09422e31171aa08f50367652149fa4a27b53cd20652e67f57d72a8","observation_id":"4a14cdd5-e996-4f5d-bf35-f7c077786f05","resolution":{"observed_at":"2026-08-06T14:43:32.761295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:32.455764Z","title":"Real risks of fake data: Synthetic data, diversity-washing and consent circumvention","venue":null,"work_id":"9c72373f-e683-4a04-9bb7-453c9f0c74f8","year":2024},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.143810Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:e043ab498cc63bcb6a7541b96a877432523b2c90587c9a5ff4b8fd0f28aae8cc","observation_id":"7f42d7f7-c46a-4d6f-8511-b603a94d9871","resolution":{"observed_at":"2026-08-06T14:43:32.559264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:32.222036Z","title":"A comprehensive review of current trends, challenges, and opportunities in text data privacy.Computers & Security, 151:104358, 2025","venue":null,"work_id":"8ca4dba0-4894-4ac0-b5c9-5ff0548105b6","year":2025},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.265955Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:1318fce5b674a0603d9ea34c4ad7f50fbb1e9a57a3b0d512c0c832dd0df9e1b7","observation_id":"a11426cc-f9d7-41d9-8d96-93e79af1b396","resolution":{"observed_at":"2026-08-06T14:43:32.354203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:32.039090Z","title":null,"venue":null,"work_id":"1f5bbd27-4e30-4c55-a660-965e98559517","year":2021},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.380157Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:4e478070573f68a49db5da7f94ec2b6edf57e02bec9d287d425d9a05500ab2d5","observation_id":"4721f788-142f-474b-90e6-cf5c614cc32a","resolution":{"observed_at":"2026-08-06T14:43:32.148452Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:31.863476Z","title":"Flair: An easy-to-use framework for state-of-the-art nlp","venue":null,"work_id":"69d40eff-a7e8-4797-a99c-fcd1af64fe94","year":2019},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.483301Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:97e0ca44b41c26674490ab6c21d6a70fc2e2cd46dfbbedd72ffc258417d26096","observation_id":"13720b7a-c11c-4037-9d57-cde4e54bae5c","resolution":{"observed_at":"2026-08-06T14:43:31.968489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:31.682663Z","title":"the\", \"is","venue":null,"work_id":"42506564-5073-4eed-a76b-fe0ef2cb9522","year":2020},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.590011Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:335449a8bf75c9152c1cba4c2ed40f3c2d266aa81c6ccb70f23841420d3e2329","observation_id":"acb65443-d06e-4cbe-a1c8-6e72b709789f","resolution":{"observed_at":"2026-08-06T14:43:31.776473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:31.469209Z","title":"Fell apart after a couple washes","venue":null,"work_id":"14c3632c-6bb9-4aa5-b535-954ac98a3dff","year":null},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.702813Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:9b2516636f062d89adfa1aa7eec29b736422eae0e1447c5bf67dc82c49ac2faf","observation_id":"2737e222-924a-48e7-bff0-95f1fd34324b","resolution":{"observed_at":"2026-08-06T14:43:31.569623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:31.209334Z","title":"my daughter,","venue":null,"work_id":"c594de74-cb92-4ad6-97f3-cc72314719ee","year":null},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.784214Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:fc99dd2a704cbb7a81d72582734d0065d4742d66579969453b807d538337691a","observation_id":"7f50b40c-401a-451e-8874-c05145b2c98f","resolution":{"observed_at":"2026-08-06T14:43:31.353849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-06T14:43:31.026986Z","title":"Figure 6 shows this distribution, where each value reflects how similar a user’s writing style is to the rest of the population","venue":null,"work_id":"65f44883-265f-4bd4-834c-00626e6d6a74","year":2000},"citing_paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T14:43:30.905284Z"},"links":{"citing_paper":"/paper/2507.18055"},"observation_digest":"sha256:dacfccd9de4f3ecbb54c5288e5ae58e57fa753ef4b8a26603eaa8ea6ac308b06","observation_id":"6759d421-0a3f-42a2-aaa1-81c9f1108541","resolution":{"observed_at":"2026-08-06T14:43:31.132461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.18055","last_updated":"2025-07-24T03:12:16Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-12T07:42:11.110843Z","submitted_at":"2025-07-24T03:12:16Z","title":"Privacy-Preserving Synthetic Review Generation with Diverse Writing Styles Using LLMs"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":19},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2507.18055."}