{"as_of":"2026-08-17T18:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91fc0f4ab0a382173111f9cb9920532eee15b95be2fc78e8b6dba6c2eb887428","coverage":[{"denominator":94,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":94,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T15:57:47.821635Z","state":"measured"},{"denominator":95,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":95,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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":"2026-06-29T06:39:55.821587Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T14:33:30.915643Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"cited_work":{"arxiv_id":"2509.10696","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.10696","snapshot_observed_at":"2026-06-29T14:33:30.915643Z","title":"Struct-bench: A benchmark for differentially private structured text generation,","venue":null,"work_id":"e9d97984-6173-4618-aed4-a47bdec157c8","year":2025},"citing_paper":{"arxiv_id":"2605.30312","last_updated":"2026-05-28T17:53:30Z","snapshot_observed_at":"2026-08-04T03:52:20.332069Z","submitted_at":"2026-05-28T17:53:30Z","title":"DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T06:39:55.821587Z"},"links":{"cited_paper":"/paper/2509.10696","citing_paper":"/paper/2605.30312"},"observation_digest":"sha256:16b7c34b46df399421e4d2246334b90553aed3d06dd06a3784d1783e624cd1e6","observation_id":"511c573f-da0a-4eb5-baf2-d251275eb9ae","resolution":{"observed_at":"2026-06-29T14:33:30.917483Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.10696/citation-record","integrity":"/paper/2509.10696/integrity","json":"/paper/2509.10696/citation-record.json","paper":"/paper/2509.10696"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:57:47.533064Z","title":"Hugging Face Datasets https:// huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/tree/ bcd32a724d8460ebe14e1d05b0195e30e9a46cb1, apr 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.533064Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:1d99f4c8293cf5bc545309bf5687859555e680deb527663458bde728bbc75a7d","observation_id":"3f37b900-f042-4cc5-8b50-d0c915d918eb","resolution":{"observed_at":"2026-08-15T15:57:47.533064Z","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":"2026-08-15T15:57:47.536737Z","title":"OpenReview.net, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.536737Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:2a94d4c87afde73f2cf7367a2fcb540cf7090621ef03f98cf14432ac206610cc","observation_id":"7b234df2-d262-4fc9-94df-5b10cb7a23dd","resolution":{"observed_at":"2026-08-15T15:57:47.536737Z","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":"2026-08-15T15:57:47.539792Z","title":"Abadi, A","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.539792Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0c3aed93f37f0349e897e3205380ebde104a794cc1ab85601f4232aa55592047","observation_id":"c94c235b-8698-4a23-b079-7323ed277e61","resolution":{"observed_at":"2026-08-15T15:57:47.539792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02467","last_updated":"2025-04-29T11:33:25Z","snapshot_observed_at":"2026-08-12T15:23:39.099360Z","submitted_at":"2024-12-03T14:10:09Z","title":"DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02467","snapshot_observed_at":"2026-08-15T15:57:47.543308Z","title":"Dp-2stage: Adapting language models as differentially private tabular data generators.arXiv preprint arXiv:2412.02467, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.543308Z"},"links":{"cited_paper":"/paper/2412.02467","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:24805120b278258cb413a5ac78a4ec8ff471fb46d5f70efdc472c417ad0c4bd3","observation_id":"fe56f477-5770-4eb4-b5cb-196d40d0f0c8","resolution":{"observed_at":"2026-08-15T15:57:47.543308Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.07740","last_updated":"2021-10-01T17:11:29Z","snapshot_observed_at":"2026-08-12T15:22:49.037671Z","submitted_at":"2020-04-16T16:24:22Z","title":"Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.07740","snapshot_observed_at":"2026-08-15T15:57:47.547146Z","title":"Really useful synthetic data–a framework to evaluate the quality of differentially private synthetic data.arXiv preprint arXiv:2004.07740, 2020","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.547146Z"},"links":{"cited_paper":"/paper/2004.07740","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:2b769b1e80da1ac2a312bad88bdf59a8aa5da17655758954e6e7a3f64766a0b0","observation_id":"292a2135-72f0-49df-b72b-0d99bd9a36f7","resolution":{"observed_at":"2026-08-15T15:57:47.547146Z","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":"2026-08-15T15:57:47.550328Z","title":"Adulthttps://doi.org/10.24432/C5XW20","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.550328Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:4ff0dcdfb3cab09b47bc4005a391d6fd0e7d4e2c6a3e05e3ab3c98fbe131fe8b","observation_id":"71e781ed-3784-456a-8ac8-869e3e8e9f6f","resolution":{"observed_at":"2026-08-15T15:57:47.550328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-15T15:57:47.553004Z","title":"Longformer: The long-document transformer.arXiv preprint arXiv:2004.05150, 2020","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.553004Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:1173b9309900131104eb6b36151d8404605e32488f46308b56b5ca7e4735f35d","observation_id":"d86d5a48-1102-432d-9642-a2f77dc3bb53","resolution":{"observed_at":"2026-08-15T15:57:47.553004Z","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":"2026-08-15T15:57:47.555506Z","title":"A universal metric for robust evaluation of synthetic tabular data.IEEE Transactions on Artificial Intelligence, 5(1):300–309, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.555506Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:d16d06964b949a8978d4797c29905dc19b3418f6fc1c5a8506785d214b881a4e","observation_id":"196e972c-bfb4-4779-972d-c09a9fc95635","resolution":{"observed_at":"2026-08-15T15:57:47.555506Z","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":"2026-08-15T15:57:47.559425Z","title":"Conditional synthetic data generation for robust machine learning applications with limited pandemic data","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.559425Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:df1d531aaaa4bfb531b890ecc069dc30a582c038eba3dc8bff10a51e0c4e0691","observation_id":"db086f03-0612-4b21-9707-cbc47dda0b29","resolution":{"observed_at":"2026-08-15T15:57:47.559425Z","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":"2026-08-15T15:57:47.563086Z","title":"Effective data generation for imbalanced learning using conditional generative adversarial networks.Expert Systems with applications, 91:464–471, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.563086Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:706f5c47303124b829cc6197f1e6df4e988585c14921923075b3eebef55631a0","observation_id":"317e025a-5d6e-4a81-9131-8db49052a5bc","resolution":{"observed_at":"2026-08-15T15:57:47.563086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.09202","last_updated":"2020-08-20T20:33:56Z","snapshot_observed_at":"2026-08-15T12:28:18.694181Z","submitted_at":"2020-08-20T20:33:56Z","title":"Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.09202","snapshot_observed_at":"2026-08-15T15:57:47.566486Z","title":"Conditional wasserstein gan-based oversampling of tabular data for imbalanced learning.arXiv preprint arXiv:2008.09202, 2020","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.566486Z"},"links":{"cited_paper":"/paper/2008.09202","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:2dcc9c8d05daaa9e1c6742234859c5d9b61db1874a8bc1b6dd53a80382efab20","observation_id":"fd35df45-5458-49b7-8b88-25780fa85f25","resolution":{"observed_at":"2026-08-15T15:57:47.566486Z","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":"2026-08-15T15:57:47.570602Z","title":"The GEM benchmark: Natural language generation, its evaluation and metrics","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.570602Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:4a1ba97cc39382a872e153a1d8c748c745adbd0c44339bf8d0b903f4bb6986e7","observation_id":"fcd5c6a0-9da5-4cdc-8338-3d32dfc3b39c","resolution":{"observed_at":"2026-08-15T15:57:47.570602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13861","last_updated":"2023-02-27T15:02:04Z","snapshot_observed_at":"2026-08-16T15:52:24.331573Z","submitted_at":"2023-02-27T15:02:04Z","title":"Differentially Private Diffusion Models Generate Useful Synthetic Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13861","snapshot_observed_at":"2026-08-15T15:57:47.573745Z","title":"Differentially private diffusion models generate useful synthetic images.arXiv preprint arXiv:2302.13861, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.573745Z"},"links":{"cited_paper":"/paper/2302.13861","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:ff1b3dc6fc644e350414680d9544cc5abc383d1eec227a31de6251cf0ede2fb8","observation_id":"b57cb7d3-6b45-43d0-8c3d-6cc73875085e","resolution":{"observed_at":"2026-08-15T15:57:47.573745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.10459","last_updated":"2022-11-18T19:00:44Z","snapshot_observed_at":"2026-08-17T16:28:37.876329Z","submitted_at":"2022-11-18T19:00:44Z","title":"A Unified Framework for Quantifying Privacy Risk in Synthetic Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10459","snapshot_observed_at":"2026-08-15T15:57:47.576889Z","title":"A unified framework for quantifying privacy risk in synthetic data.arXiv preprint arXiv:2211.10459, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.576889Z"},"links":{"cited_paper":"/paper/2211.10459","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:7788b2a7dd8c4c9d8a0c52993cb5fadf8938e0aee0d4aab73c1236c860d696be","observation_id":"eb0d4c5b-4acc-408f-ad49-e45458e92420","resolution":{"observed_at":"2026-08-15T15:57:47.576889Z","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":"2026-08-15T15:57:47.579930Z","title":"Benchmarking fraud detectors on private graph data.KDD, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.579930Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:db2ca3d3e5ddfff574eacff6c4ec7871f38df824d92e77a2dfbae66e95638bcf","observation_id":"1e1792c3-7a4c-47b8-8dfe-e1a4bac8a030","resolution":{"observed_at":"2026-08-15T15:57:47.579930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14681","last_updated":"2025-08-28T18:20:54Z","snapshot_observed_at":"2026-08-16T12:48:50.995619Z","submitted_at":"2025-03-18T19:37:35Z","title":"DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14681","snapshot_observed_at":"2026-08-15T15:57:47.583228Z","title":"Dpimagebench: A unified benchmark for differentially private image synthesis.arXiv preprint arXiv:2503.14681, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.583228Z"},"links":{"cited_paper":"/paper/2503.14681","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:d82b80db24c5c6d820ef60faa0c2486b4d784f51214e638d2bf65fa099e3eff9","observation_id":"73213db6-87f6-4053-a35b-98a7610dc660","resolution":{"observed_at":"2026-08-15T15:57:47.583228Z","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":"2026-08-15T15:57:47.587058Z","title":"An llm-based framework for synthetic data generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.587058Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:ee73693d3c9e3609e6fa1b64b07ef030cc517c7f2ee40e9ba8669c5a3accc504","observation_id":"a63b4b01-db0b-487b-b8f2-755209056d4e","resolution":{"observed_at":"2026-08-15T15:57:47.587058Z","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":"2026-08-15T15:57:47.589801Z","title":"Synthfair: Ensuring subgroup fairness in classification via synthetic data generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.589801Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:6d02a7cf81f27c60572dd35eb7b16f729ce95ab0aa19a0510eb6c3c0c63577d1","observation_id":"2fbcd180-a5fa-4750-8b7b-e0b67c80b784","resolution":{"observed_at":"2026-08-15T15:57:47.589801Z","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":"2026-08-15T15:57:47.593584Z","title":"Synthetic tabular data evaluation in the health domain covering resemblance, utility, and privacy dimensions.Methods of information in medicine, 62(S 01):e19–e38, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.593584Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:d8852d8b935eb097658838857e76728da43d732d1c7f4e8411a732482aa60bad","observation_id":"e9fed851-8506-4d8b-a44a-94de8f43c04b","resolution":{"observed_at":"2026-08-15T15:57:47.593584Z","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":"2026-08-15T15:57:47.596264Z","title":"Heusel, H","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.596264Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:827576583ea352d3eec12be45c5564252c88817f058009e5a8fa106ca87d0066","observation_id":"5d180b12-45b9-469f-bdd4-f9a89029c19f","resolution":{"observed_at":"2026-08-15T15:57:47.596264Z","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":"2026-08-15T15:57:47.598779Z","title":"Introduction to automata theory, languages, and computation.Acm Sigact News, 32(1):60–65, 2001","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.598779Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:555cec6db3ef7a8fc5c75057ea30738f471353a8cbb90d99f42ae1d75f802e31","observation_id":"0955f193-75ae-4afc-bab6-1dfa3e9983be","resolution":{"observed_at":"2026-08-15T15:57:47.598779Z","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":"2026-08-15T15:57:47.601492Z","title":"Pre-text: training language models on private federated data in the age of llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.601492Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:ea636005dd201979f46848a8673e7205548a5c613838de5e0f7d765be22b4514","observation_id":"e3af8b33-f2fc-4ccb-98d1-1dc095aeb00a","resolution":{"observed_at":"2026-08-15T15:57:47.601492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16438","last_updated":"2025-08-19T17:56:16Z","snapshot_observed_at":"2026-08-16T14:49:12.099350Z","submitted_at":"2025-04-23T05:57:20Z","title":"POPri: Private Federated Learning using Preference-Optimized Synthetic Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16438","snapshot_observed_at":"2026-08-15T15:57:47.603991Z","title":"Private federated learning using preference-optimized synthetic data.arXiv preprint arXiv:2504.16438, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.603991Z"},"links":{"cited_paper":"/paper/2504.16438","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:b4816a41b5b62a83559ff0f3454844ff731d235d51d26f9c8b764ccd4db3e2a7","observation_id":"a911f33c-41a6-4f27-a5e9-a4c56fbc6c8c","resolution":{"observed_at":"2026-08-15T15:57:47.603991Z","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":"2026-08-15T15:57:47.606596Z","title":"Sok: Privacy-preserving data synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.606596Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:df641ec7fe7adef7afa86570f62af3455b131803e9a260de3d0b670114b1422e","observation_id":"56a07bec-fac9-4593-9ddb-384a3140a098","resolution":{"observed_at":"2026-08-15T15:57:47.606596Z","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":"2026-08-15T15:57:48.490816Z","title":"Kynkäänniemi, T","venue":null,"work_id":"9de05b33-0d5c-4a7c-9fe5-a511800d0556","year":2019},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.609300Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:bae33dfb1aef71a9d493ae94cba9f005f6f9551da41f013d0e7e22a7cc1dee53","observation_id":"ab24d3a5-7379-4fd0-9670-a6c369029b3a","resolution":{"observed_at":"2026-08-15T15:57:48.493686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.481344Z","title":"Tregex and tsurgeon: Tools for querying and manipulating tree data structures","venue":null,"work_id":"78433092-eb16-46a4-8ca1-95ffb75fd7b1","year":2006},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.611890Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:815562ca6a1d6b2224018067b5d8e3ac1478023a0e1a9f3dcc6aa80d113c0d4f","observation_id":"6a897724-d774-4474-bec2-401b8358d58b","resolution":{"observed_at":"2026-08-15T15:57:48.485192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.472105Z","title":null,"venue":null,"work_id":"962e30e9-ab0b-479e-bf97-a093f0f9b7c2","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.614142Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:a20fd51ec62d24a00f4f98e0c657c4bcd757ef307051bf061692df140afaf33b","observation_id":"f6482bae-3ae2-4042-b986-fc75a6b836e9","resolution":{"observed_at":"2026-08-15T15:57:48.475554Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05505","last_updated":"2025-05-20T04:05:24Z","snapshot_observed_at":"2026-08-17T13:26:10.786750Z","submitted_at":"2025-02-08T09:50:30Z","title":"Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05505","snapshot_observed_at":"2026-08-15T15:57:47.617364Z","title":"Differentially private synthetic data via apis 3: Using simulators instead of foundation model.arXiv preprint arXiv:2502.05505, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.617364Z"},"links":{"cited_paper":"/paper/2502.05505","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:91d998f71a06455e0350765536e341f91d9a490df038e50ca81f00a177d60dec","observation_id":"eb5eb492-4961-46e0-98cf-38b4d7c863cb","resolution":{"observed_at":"2026-08-15T15:57:47.617364Z","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":"2026-08-15T15:57:48.463861Z","title":"Differentially private synthetic data via foundation model apis 1: Images","venue":null,"work_id":"cfee1ccd-b6f8-487c-ac77-ffcc0cf4ea36","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.620814Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:6e530eb9c1812834f96149ccfccf77b39457fe497cabcfd7ae141dc44a58d78e","observation_id":"8acc2e5f-08f5-494b-b7d3-e5184435bd7a","resolution":{"observed_at":"2026-08-15T15:57:48.467057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.454971Z","title":"Using gans for sharing networked time series data: Challenges, initial promise, and open questions","venue":null,"work_id":"7e33017b-6384-4f99-a8e6-2da15287ddc1","year":2020},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.623289Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:19e464155212251ce01a7e2a135f86944b1cedc0f156ae414cb2b17324040b40","observation_id":"72dd6eff-b6cb-4319-bf6f-c831b14a879e","resolution":{"observed_at":"2026-08-15T15:57:48.457881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.446059Z","title":"Summary statistic privacy in data sharing","venue":null,"work_id":"a10be153-4f63-44d9-af7a-3f84e69bf954","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.625805Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:ebdbcf6fc158b7aa5e6dda47d1912cef1546307890cf9ddbd5b084f68de85b8b","observation_id":"b7a2eb48-bc2f-441a-9539-da23f814b3a6","resolution":{"observed_at":"2026-08-15T15:57:48.449179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.437365Z","title":"Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.Computer Methods and Programs in Biomedicine, page 108571, 2024","venue":null,"work_id":"dc32df66-964b-4f08-8567-36838b15be36","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.628252Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:d45329ec521db58640de0b4024b2dac37fa67a72d78ed8e148f833a185b426da","observation_id":"7f257d3f-2d15-4e9a-9167-9f8d376546f5","resolution":{"observed_at":"2026-08-15T15:57:48.440645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:47.630600Z","title":"An evaluation framework for synthetic data generation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.630600Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:7d8a08f60f5a6ddc2ac9e7913069135150df1719983949c48084cd4e1b9ce826","observation_id":"88dd5cb1-85b9-4ccc-81ee-acd84db5183a","resolution":{"observed_at":"2026-08-15T15:57:47.630600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04055","last_updated":"2026-05-17T16:04:18Z","snapshot_observed_at":"2026-08-14T05:08:48.854146Z","submitted_at":"2025-02-06T13:13:26Z","title":"Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04055","snapshot_observed_at":"2026-08-15T15:57:47.632994Z","title":"Evaluating inter-column logical relationships in synthetic tabular data generation.arXiv preprint arXiv:2502.04055, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.632994Z"},"links":{"cited_paper":"/paper/2502.04055","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:e4d5bd77332eeccedb52a3d5934cb31eb23a56cfbcff7fd4ee8c3136ed5bcc8d","observation_id":"d4889845-d067-4693-93d5-bbb08c6cb124","resolution":{"observed_at":"2026-08-15T15:57:47.632994Z","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":"2026-08-15T15:57:48.422365Z","title":"PhD thesis, Politecnico di Torino, 2025","venue":null,"work_id":"a28f7d45-f92c-4b7c-a925-21ce28fe1c04","year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.635167Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:cb8e945755c5e15e611b35aa4aacad14b93e9e26253c034d07260c62495cf2a6","observation_id":"15543bbb-ce6d-4aba-9a33-94f3a6ecc722","resolution":{"observed_at":"2026-08-15T15:57:48.426175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12677","last_updated":"2024-06-13T01:53:55Z","snapshot_observed_at":"2026-08-16T17:24:58.710855Z","submitted_at":"2022-01-29T23:02:24Z","title":"AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12677","snapshot_observed_at":"2026-08-15T15:57:47.637359Z","title":"Aim: An adaptive and iterative mechanism for differentially private synthetic data.arXiv preprint arXiv:2201.12677, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.637359Z"},"links":{"cited_paper":"/paper/2201.12677","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:3e6146338a1ac1e767cca065ebaaefc43aad766f055c3166c5b9b95692db9e8d","observation_id":"877017a2-b475-4338-b339-4b0c645303c0","resolution":{"observed_at":"2026-08-15T15:57:47.637359Z","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":"2026-08-15T15:57:48.412562Z","title":"Benchmarking evaluation protocols for classifiers trained on differentially private synthetic data.IEEE Access, 2024","venue":null,"work_id":"5237f0d4-4563-4fbb-bee6-c4ace92e6242","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.639644Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:34361c57b3ac9e2ea1e3e7a52b599df1653f15cab341360c14795dc80b8c1f50","observation_id":"680f330e-29b1-40ec-be66-d8db3f5bd617","resolution":{"observed_at":"2026-08-15T15:57:48.416468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.20641","last_updated":"2024-12-30T01:10:10Z","snapshot_observed_at":"2026-08-12T15:23:14.013780Z","submitted_at":"2024-12-30T01:10:10Z","title":"SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.20641","snapshot_observed_at":"2026-08-15T15:57:47.641721Z","title":"Safesynthdp: Leveraging large language models for privacy-preserving synthetic data generation using differential privacy.arXiv preprint arXiv:2412.20641, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.641721Z"},"links":{"cited_paper":"/paper/2412.20641","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:42c45e8030e7ef0b61ebfa6adfc43ed102dc6d8520d9a65b0850290551940268","observation_id":"e29af933-3f90-4b2f-a8d2-fa3399d19c5a","resolution":{"observed_at":"2026-08-15T15:57:47.641721Z","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":"2026-08-15T15:57:47.643798Z","title":"Synthetic data for privacy-preserving clinical risk prediction.Scientific Reports, 14(1):25676, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.643798Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0578399096383f6d47275d701c390aaf8e8efac881432c02db8a10efd090c42a","observation_id":"ca4c9ae9-ea26-482e-bf03-023cae46d8db","resolution":{"observed_at":"2026-08-15T15:57:47.643798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08327","last_updated":"2024-10-10T19:31:02Z","snapshot_observed_at":"2026-08-16T13:10:33.131764Z","submitted_at":"2024-10-10T19:31:02Z","title":"Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains","version":1},"cited_work":{"arxiv_id":"2410.08327","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.08327","snapshot_observed_at":"2026-08-15T15:57:47.992879Z","title":"Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains","venue":"cs.CL","work_id":"d69127af-4bda-4016-b36e-0d03b7fa9639","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.646409Z"},"links":{"cited_paper":"/paper/2410.08327","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:c705027c56547a5b4cd65fdb79413e6ffc1d6e03c17d772f24c08b7e2b3942b4","observation_id":"32556448-08e3-4f59-9fe1-6c97391d3a28","resolution":{"observed_at":"2026-08-15T15:57:47.996435Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.400858Z","title":"Type/token ratios: What do they really tell us?Journal of child language, 14(2):201–209, 1987","venue":null,"work_id":"4634e953-36e5-4dfc-9b95-b106e7b4d65b","year":1987},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.648772Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:5ce347068823e60643d5e56c6ba6cc7a392cd59e618a783d66a93f5481906fde","observation_id":"9b15b060-900d-496e-9d52-fb360c9cd290","resolution":{"observed_at":"2026-08-15T15:57:48.403647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.05537","last_updated":"2020-11-11T04:03:08Z","snapshot_observed_at":"2026-08-16T19:06:41.859160Z","submitted_at":"2020-11-11T04:03:08Z","title":"Differentially Private Synthetic Data: Applied Evaluations and Enhancements","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.05537","snapshot_observed_at":"2026-08-15T15:57:47.651125Z","title":"Differ- entially private synthetic data: Applied evaluations and enhancements.arXiv preprint arXiv:2011.05537, 2020","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.651125Z"},"links":{"cited_paper":"/paper/2011.05537","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:cdbba5cd608ca111c11c167905be7b29fb83ee89415f80b81fc7762557a4b6eb","observation_id":"8445ec72-013f-486a-944b-a0c652f8d38c","resolution":{"observed_at":"2026-08-15T15:57:47.651125Z","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":"2026-08-15T15:57:47.653698Z","title":"Assessing generative models via precision and recall.Advances in neural information processing systems, 31, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.653698Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:d74e31536d14a8acd6b8b2d07ffd41b03c3399d997b4c6ffd5b6a95816aa8e96","observation_id":"15340ec7-1122-4fa7-a74b-d9c88c2fd451","resolution":{"observed_at":"2026-08-15T15:57:47.653698Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.20846","last_updated":"2025-03-26T16:06:33Z","snapshot_observed_at":"2026-08-17T14:13:09.633888Z","submitted_at":"2025-03-26T16:06:33Z","title":"Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.20846","snapshot_observed_at":"2026-08-15T15:57:47.656528Z","title":"Generating synthetic data with formal privacy guarantees: State of the art and the road ahead.arXiv preprint arXiv:2503.20846, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.656528Z"},"links":{"cited_paper":"/paper/2503.20846","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:ebec9d3b1d9155ade15b19fd96d41acd2e6c24ed92c876a89bdeaee85dbcc2ba","observation_id":"5a9a5445-40e9-44e4-a91a-219293413ef2","resolution":{"observed_at":"2026-08-15T15:57:47.656528Z","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":"2026-08-15T15:57:48.389069Z","title":"Ai for data science: A benchmark for differentially private text dataset generators","venue":null,"work_id":"90863799-81b6-4767-a100-f9fc6eadd1ab","year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.659694Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:8f827b99ff400f6f56d4e5982d9178dd794978894412d6e25d392293021dfaf6","observation_id":"a8eb216d-0fbe-4413-b820-dffd2deaf3f4","resolution":{"observed_at":"2026-08-15T15:57:48.392701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:47.663502Z","title":"On the foundations of quantitative information flow","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.663502Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:a31fff682e2689fd039793cd3080abfd016ec42cc4c0b92e32481913231534cc","observation_id":"e295ad51-f7f6-4f07-a057-385685658c94","resolution":{"observed_at":"2026-08-15T15:57:47.663502Z","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":"2026-08-15T15:57:48.375423Z","title":"Evaluation is key: a survey on evaluation measures for synthetic time series.Journal of Big Data, 11(1):66, 2024","venue":null,"work_id":"84af5275-9119-4cfa-a226-dc74dd6865f2","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.666676Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:d57fe67c45d96b6529aabd61841b7bd60c34709722db90cb587dce7ee2e4d366","observation_id":"046499cf-7cc1-4dac-acd5-8b2a5853d07d","resolution":{"observed_at":"2026-08-15T15:57:48.378288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.06024","last_updated":"2022-07-05T13:54:26Z","snapshot_observed_at":"2026-08-16T18:40:22.907188Z","submitted_at":"2021-09-13T14:54:39Z","title":"Formalizing and Estimating Distribution Inference Risks","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.06024","snapshot_observed_at":"2026-08-15T15:57:47.670973Z","title":"Formalizing and estimating distribution inference risks.arXiv preprint arXiv:2109.06024, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.670973Z"},"links":{"cited_paper":"/paper/2109.06024","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:bc29b965e5d235e2ef55bd3ab72c81ebabae3a4a7782bf61c49447311c8bb18c","observation_id":"5a2228e5-a22c-4234-8d11-810158442188","resolution":{"observed_at":"2026-08-15T15:57:47.670973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12347","last_updated":"2025-07-17T01:39:41Z","snapshot_observed_at":"2026-08-16T12:49:40.810268Z","submitted_at":"2025-03-16T04:00:32Z","title":"Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.12347","snapshot_observed_at":"2026-08-15T15:57:47.675513Z","title":"Synthesizing privacy-preserving text data via finetuning without finetuning billion-scale llms.arXiv preprint arXiv:2503.12347, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.675513Z"},"links":{"cited_paper":"/paper/2503.12347","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:5fcd220d8cc02879d5b39636b6b5a18080725bfbb4bda3932f18419638307634","observation_id":"f7856163-c7f9-4d06-b42f-d22a390a6433","resolution":{"observed_at":"2026-08-15T15:57:47.675513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.11765","last_updated":"2024-01-28T00:24:10Z","snapshot_observed_at":"2026-08-16T14:58:55.623232Z","submitted_at":"2023-09-21T03:59:00Z","title":"Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.11765","snapshot_observed_at":"2026-08-15T15:57:47.678661Z","title":"Privacy-preserving in-context learning with differentially private few-shot generation.arXiv preprint arXiv:2309.11765, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.678661Z"},"links":{"cited_paper":"/paper/2309.11765","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:66f247596ffb0a051ba8587850764bc65f565397edf11765bac1d480bf9b190a","observation_id":"3bf7f063-0201-4f5c-9875-b234922f904b","resolution":{"observed_at":"2026-08-15T15:57:47.678661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.09238","last_updated":"2022-02-15T16:08:22Z","snapshot_observed_at":"2026-08-16T17:33:55.283937Z","submitted_at":"2021-12-16T22:49:53Z","title":"Benchmarking Differentially Private Synthetic Data Generation Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.09238","snapshot_observed_at":"2026-08-15T15:57:47.681588Z","title":"Benchmarking differentially private synthetic data generation algorithms.arXiv preprint arXiv:2112.09238, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.681588Z"},"links":{"cited_paper":"/paper/2112.09238","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:aae60f41ad2f09589b1421a3a6fcf83005e5fc7f39115d3905c91395453ffce5","observation_id":"dbcd063c-2cdc-404f-af01-557c6fcdc692","resolution":{"observed_at":"2026-08-15T15:57:47.681588Z","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":"2026-08-15T15:57:48.364586Z","title":"Water Bottle Dataset - Flipkart https://www.kaggle.com/datasets/tharunmss/ water-bottle-dataset-flipkart","venue":null,"work_id":"3798a1d4-9e3a-47f5-b7fd-27621fc7fa23","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.684894Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0e455ccc32208b9b24bed10bc0860f2b61983d050fcf07c51ac8d97f8db30b9c","observation_id":"116dbc7d-f6e0-4c3d-b878-b90baab7b615","resolution":{"observed_at":"2026-08-15T15:57:48.368613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.08842","last_updated":"2024-12-12T00:40:54Z","snapshot_observed_at":"2026-08-17T17:17:48.179841Z","submitted_at":"2024-12-12T00:40:54Z","title":"Kajal: Extracting Grammar of a Source Code Using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.08842","snapshot_observed_at":"2026-08-15T15:57:47.687772Z","title":"Kajal: Extracting grammar of a source code using large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.687772Z"},"links":{"cited_paper":"/paper/2412.08842","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:7157b94c7e681ccf4cdd79ad08b10d73bb02b50c2ad8ea9cd8be997f78b7da4b","observation_id":"aa418dd2-a89b-4f07-b9f6-20296a2a422c","resolution":{"observed_at":"2026-08-15T15:57:47.687772Z","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":"2026-08-15T15:57:48.356474Z","title":"Dp-cgan: Differentially private synthetic data and label generation","venue":null,"work_id":"1e48f219-5d22-47ec-a178-837ff81dddb6","year":2019},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.692788Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:1fefa5dfc4030c8478ba2305ef9fd8863be28e3c31d17f832400b44816af429d","observation_id":"d8d75af5-bf16-4871-9113-a93cfc7e0b05","resolution":{"observed_at":"2026-08-15T15:57:48.359416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01457","last_updated":"2024-06-03T15:43:57Z","snapshot_observed_at":"2026-08-16T13:46:40.359422Z","submitted_at":"2024-06-03T15:43:57Z","title":"Differentially Private Tabular Data Synthesis using Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01457","snapshot_observed_at":"2026-08-15T15:57:47.696435Z","title":"Differentially private tabular data synthesis using large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.696435Z"},"links":{"cited_paper":"/paper/2406.01457","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:578eaa9bf831ebed9c9135b35ec8039d70e1b54e23a9e751b7f1d7f4ede0137a","observation_id":"3574664b-25d0-44d8-8c04-fd79db908ef1","resolution":{"observed_at":"2026-08-15T15:57:47.696435Z","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":"2026-08-15T15:57:48.344841Z","title":"Synthetic data, real errors: how (not) to publish and use synthetic data","venue":null,"work_id":"ebd2a347-6039-4bf2-a938-4a0a5896242d","year":2023},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.699859Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:314ccd7e9aadf0c35b896d8b8d0de9dfc00e0bb3c706dbd14146d89b276c58cf","observation_id":"95318f4d-4f0d-4b35-9c01-ad78c289678a","resolution":{"observed_at":"2026-08-15T15:57:48.348921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:47.704364Z","title":"Synthesize privacy-preserving high-resolution images via private textual intermediaries.arXiv preprint arXiv:2506.07555, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.704364Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:59aaf6f8ae6a92b2e7c21021ed0aa325d8bf95c00b43783b0397e28c3cb59361","observation_id":"c3b6cfe5-01b0-45ac-89c2-c94f913c9b41","resolution":{"observed_at":"2026-08-15T15:57:47.704364Z","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":"2026-08-15T15:57:48.335741Z","title":"Statistic maximal leakage","venue":null,"work_id":"58d5cdc6-365d-40f3-a680-5d22f8c72f56","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.708317Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:8d52e69ce5f4fc5471e937d94d67faeda4414d66a1bd4fb27f55879675ba00e8","observation_id":"377d26e7-399d-45b4-a44e-e233f4ac00e9","resolution":{"observed_at":"2026-08-15T15:57:48.338996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.324827Z","title":"dp-transformers: Training transformer models with differential privacy, 2022","venue":null,"work_id":"42b1b0e5-45ad-4ceb-b7e7-cb78230b0153","year":2022},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.710824Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:605b90f3677349bb45ad307b6ab6cc65658ad6e8a9877be9dd4289febc3e4e64","observation_id":"4309af01-fa86-4c49-80f6-81e2f7e47c2e","resolution":{"observed_at":"2026-08-15T15:57:48.328895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:47.713435Z","title":"Differentially private synthetic data via foundation model apis 2: Text","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.713435Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:61cb196eec104f9345b60862f8854086484b78716e74104d9817ce34ea797195","observation_id":"908f8be3-d37d-4a8f-aa21-b69591619ad7","resolution":{"observed_at":"2026-08-15T15:57:47.713435Z","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":"2026-08-15T15:57:48.311963Z","title":"Generation and evaluation of privacy preserving synthetic health data.Neurocomputing, 416:244–255, 2020","venue":null,"work_id":"d4383007-aaea-47d9-a05a-a14f42c1c347","year":2020},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.716324Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:f3a1b709675c561e1641ecc35ddb918e60761e2c728d82e66352d8be54d8d80b","observation_id":"ba027625-994f-4e20-a782-d1e5200329b8","resolution":{"observed_at":"2026-08-15T15:57:48.315480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10424","last_updated":"2024-03-29T13:48:44Z","snapshot_observed_at":"2026-08-16T19:37:14.371218Z","submitted_at":"2024-03-15T15:58:37Z","title":"Structured Evaluation of Synthetic Tabular Data","version":2},"cited_work":{"arxiv_id":"2403.10424","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.10424","snapshot_observed_at":"2026-08-15T15:57:47.871441Z","title":"Structured Evaluation of Synthetic Tabular Data","venue":"cs.LG","work_id":"38e8f1e4-c62f-4e46-b435-5949df6db1c0","year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.719952Z"},"links":{"cited_paper":"/paper/2403.10424","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:ee734c8b7e159dc5f687f3a282d7ce6b987ec11e46563e068168e19e3929b4ff","observation_id":"7909e35e-30cd-4db7-bb8d-8d425b2e4f86","resolution":{"observed_at":"2026-08-15T15:57:47.876735Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.06500","last_updated":"2022-07-14T22:14:17Z","snapshot_observed_at":"2026-08-16T17:49:45.358995Z","submitted_at":"2021-10-13T05:15:00Z","title":"Differentially Private Fine-tuning of Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.06500","snapshot_observed_at":"2026-08-15T15:57:47.723444Z","title":"Differentially private fine-tuning of language models.arXiv preprint arXiv:2110.06500, 2021","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.723444Z"},"links":{"cited_paper":"/paper/2110.06500","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:3d16ca208b990c99b24faad701852574bc269a59bd414e25f326929a6556bb17","observation_id":"2b6312ba-21e9-4301-9889-70bf219e4091","resolution":{"observed_at":"2026-08-15T15:57:47.723444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14445","last_updated":"2025-07-24T03:19:19Z","snapshot_observed_at":"2026-08-16T13:59:17.948466Z","submitted_at":"2024-04-20T08:08:28Z","title":"A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14445","snapshot_observed_at":"2026-08-15T15:57:47.726247Z","title":"A multi-faceted evaluation framework for assessing synthetic data generated by large language models.arXiv preprint arXiv:2404.14445, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.726247Z"},"links":{"cited_paper":"/paper/2404.14445","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:74c6d398f2313d62015b1117a526debea73f12745c3cf707e83ee1b9ff8b3473","observation_id":"4ed7a9bf-20f5-4170-9978-31488525a921","resolution":{"observed_at":"2026-08-15T15:57:47.726247Z","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":"2026-08-15T15:57:48.305253Z","title":null,"venue":null,"work_id":"7fb9e8b2-4e8c-4477-9fdf-a65417aa5e83","year":2023},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.729305Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:8fdce45ac4614ea9c77ace132e79a41008eb3ac8354b4fd4738648f2821e1e99","observation_id":"b165bfc2-d0ea-4575-86e4-bfae3831fbb8","resolution":{"observed_at":"2026-08-15T15:57:48.307749Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.295641Z","title":"In30th USENIX Security Symposium (USENIX Security 21), pages 929–946, 2021","venue":null,"work_id":"3da23d4c-ee4f-4108-83e5-c411031699a9","year":2021},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.733051Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0d0c39e823965cb18f642a58fbf48f2a66bb266cb2e01072e25b90a2026f0e32","observation_id":"e1c3b815-bc05-4f55-b016-2b659971f45e","resolution":{"observed_at":"2026-08-15T15:57:48.298992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.288894Z","title":"Zheng, W.-L","venue":null,"work_id":"a641d2ae-1104-4050-9e99-32e74c1f649c","year":2023},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.735532Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:63674bcea461bd83f172f19f147bec875cac304fe3bfbb83912ae6eba8251114","observation_id":"85171f00-ba42-413f-9e5b-7c8791b00db0","resolution":{"observed_at":"2026-08-15T15:57:48.291514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00245","last_updated":"2025-02-01T00:54:25Z","snapshot_observed_at":"2026-08-09T23:06:19.225107Z","submitted_at":"2025-02-01T00:54:25Z","title":"Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00245","snapshot_observed_at":"2026-08-15T15:57:47.737864Z","title":"Which nodes are central to our downstream tasks, and which nodes are semantically related to them?","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.737864Z"},"links":{"cited_paper":"/paper/2502.00245","citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:85f82e4f0aa33d5f4622f646e6e774a9ca6052d3d1f8136efb191eb91dad8955","observation_id":"56e2f21e-1c0b-49e3-857b-f85af9d66c89","resolution":{"observed_at":"2026-08-15T15:57:47.737864Z","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":"2026-08-15T15:57:48.272862Z","title":null,"venue":null,"work_id":"48e657a4-9551-417a-aa86-48e13a47c993","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.743656Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:e596a3994c3e9653d7036e8da1c7998f7350975ef5556bc36442230cb9c1d640","observation_id":"6be85f33-6861-4c84-bb97-c22c82994c73","resolution":{"observed_at":"2026-08-15T15:57:48.275498Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.264990Z","title":null,"venue":null,"work_id":"7f9e2061-7b02-4d33-a7e1-007bfbd50b39","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.746680Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:551162769f879e41aed1bbfa3f472a73d4c89a85120f50f573cd9a7f261bf8c3","observation_id":"8e519fc3-9b73-4c62-89ef-1b4554b9131c","resolution":{"observed_at":"2026-08-15T15:57:48.268173Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.257893Z","title":null,"venue":null,"work_id":"e3a1725f-1c12-4dfc-92db-d3bd450ef32f","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.749322Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:e92d858fee1c8717cffb32ac5cab5f2d8a2ad3ef96a454d751f965af59b7f1b3","observation_id":"f4bb765b-9c8b-46b6-969d-623221b9b17f","resolution":{"observed_at":"2026-08-15T15:57:48.260711Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.251278Z","title":null,"venue":null,"work_id":"dfc6ba77-0dcf-441e-b549-4801483b2fa1","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.751931Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:d8c22adc177349d36efcf70a7a8a61386a1300b23866c8a811d019c52fe56cdb","observation_id":"926988ca-aa51-42e5-9775-3248dddbbe77","resolution":{"observed_at":"2026-08-15T15:57:48.253872Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.242842Z","title":null,"venue":null,"work_id":"34440e37-1ee1-46d9-817e-5bb0ff589db5","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.755787Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:6714f9dda98e0ad708324769c46837cd579e2466d8a0c1196332bb57c3c0d15a","observation_id":"9244cf59-e876-4866-9462-4d177c7a1983","resolution":{"observed_at":"2026-08-15T15:57:48.246308Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.234937Z","title":null,"venue":null,"work_id":"adfe4e86-318c-4a65-b982-d8dc3b817901","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.758481Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:02cb4cd3f68ae938ff4a490fe5d6d1aec0088efadb3c4141ae520f10e4e7de9f","observation_id":"5c1cecdf-138e-42bf-831a-17b475133071","resolution":{"observed_at":"2026-08-15T15:57:48.238117Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.225976Z","title":null,"venue":null,"work_id":"dba55b07-5c3a-4a14-b599-f455b1d9584f","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.761930Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0951045754ed689fe79a6d5eb3b00d5daed6868f903125b8a24ad1a1b1bb9753","observation_id":"913ca51e-1832-43a0-bb66-dec6e78c61f9","resolution":{"observed_at":"2026-08-15T15:57:48.229704Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.217926Z","title":null,"venue":null,"work_id":"5835fd80-c705-47d3-960b-ca86797bc093","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.765511Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:a05e2b57fd9915c5e63e03b80127c6280495aec3ae7df806c20b6d5b2cae0a9d","observation_id":"487ef7c1-f9df-4b91-8fb2-d29c99e90aae","resolution":{"observed_at":"2026-08-15T15:57:48.221104Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.209319Z","title":null,"venue":null,"work_id":"0ef0713c-7124-4415-8c3c-49dcdcd86d6a","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.768358Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:71162509480f04bcd8cae946b62f716cd3a465ece28f6b96070177b598b1f95e","observation_id":"65e3e7ad-15f5-4004-a025-7c7020f738a3","resolution":{"observed_at":"2026-08-15T15:57:48.213739Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.202598Z","title":null,"venue":null,"work_id":"af70e43b-0bd6-41ee-b9ef-d2e3331dc2e5","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.771843Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:11a01d7ec7ba2142a4d8c68a61833628f9e0b3b1a691896aa923dd2ca0bc277b","observation_id":"9d7efc23-c718-4e94-966b-e9671e8425ef","resolution":{"observed_at":"2026-08-15T15:57:48.205142Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.195611Z","title":null,"venue":null,"work_id":"ccb92aed-f022-4718-bd90-494a8d278329","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.775247Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0f32d8d2d3c39d9f8e911686d0ee20d3c0d7eb27fbd1c81db0b6770b123003de","observation_id":"f9f6a5e1-bec5-4735-92e0-8046dde71cb8","resolution":{"observed_at":"2026-08-15T15:57:48.198000Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.188680Z","title":null,"venue":null,"work_id":"4090c132-168d-406c-8057-c0a132d7ad06","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.777778Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:7bba9fe5d122b274581743e347beeb8c511d32f48cb37210fbcf33f2696ed804","observation_id":"cae6c28d-1c26-4f6d-a022-00fb241928ff","resolution":{"observed_at":"2026-08-15T15:57:48.191512Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.181949Z","title":null,"venue":null,"work_id":"351ab75a-c382-41cd-bc77-c4f1cbd4440c","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.781014Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:cdec71e04c847076f8b0a7ad691269e3db61843e9c99fafe0b4fa36fbece667b","observation_id":"b2554376-dc53-4913-ac29-4d0442c66cac","resolution":{"observed_at":"2026-08-15T15:57:48.184235Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.173606Z","title":null,"venue":null,"work_id":"b5f4b7bd-c68f-43b9-b040-8a1f2c19414e","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.784659Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:7d3b97d8de55bd009dc2194318407d82bde679bfcb9e303365ade9a333c2bd3a","observation_id":"5ae18b85-8303-4afb-9dae-e447fdbeee37","resolution":{"observed_at":"2026-08-15T15:57:48.176879Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.162915Z","title":null,"venue":null,"work_id":"fdcc937b-6267-49db-b568-5e2fc5fd32f9","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.788006Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:b27fa96cd8137c9ad384cb842a030a28121163754e7c811d20ecc1521bc5dd49","observation_id":"82812627-f5ad-4701-bb31-191da298baf7","resolution":{"observed_at":"2026-08-15T15:57:48.165910Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.152692Z","title":null,"venue":null,"work_id":"041accfa-0faa-4b3c-939e-34dcc5704842","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.790698Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:df7fcaafd5099e6bd2163bed09c1d88505746e532aa5b27988620e45ad2fcefa","observation_id":"48a7e866-e7e6-4553-9f09-b51f5ddfa71a","resolution":{"observed_at":"2026-08-15T15:57:48.155748Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.143548Z","title":null,"venue":null,"work_id":"a057ec19-bc26-410d-981c-13555b7894cf","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.794488Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:4b03ca560fda3bdfc6b0dfea5e52c8cfd1d5c7cef0d7a113a1c5e04d40d6f0de","observation_id":"13c1bc06-fbe1-4cd2-b532-486c31173cff","resolution":{"observed_at":"2026-08-15T15:57:48.146542Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.134312Z","title":null,"venue":null,"work_id":"94d322e4-6a6f-4b6e-9772-bae327e94ba1","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.797607Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0dba7f35e72f84555ae77a8f93f1af5be2dc3d0218178f1ae3889af1013f668b","observation_id":"7581e696-23b3-48c3-b190-133a2595f76f","resolution":{"observed_at":"2026-08-15T15:57:48.137271Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.126578Z","title":"We prepend the instructions to each training sample and fine-tune the foundation model for 20 epochs with batch size 32, weight decay 0.01, and learning rate10−4","venue":null,"work_id":"22b05ef9-3e20-42c7-b271-57d5580e54c0","year":1900},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.801312Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:1ee2a2b948749ccbe8a636bcfd463ff0bd5e06532d3b324f0b62835f490564ab","observation_id":"c1d358a6-1d67-4804-b7b7-a491815d910f","resolution":{"observed_at":"2026-08-15T15:57:48.129950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.117909Z","title":null,"venue":null,"work_id":"fed1861e-74aa-4911-afcf-9c79f10be90e","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.804996Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:fdd95944e0527d936e446a4f02a58159bd2f791c75f68ad6f16640247e711022","observation_id":"8c902962-4277-494f-a1a1-bc70306eee89","resolution":{"observed_at":"2026-08-15T15:57:48.121091Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.108159Z","title":null,"venue":null,"work_id":"75cda580-4801-4404-b3fa-5f415108d39a","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.808539Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:0994fe7ddffa0f5c9e63171614e4a8a81c476d0d913154cd9c93c02ce4e2a48f","observation_id":"f091d6ba-2236-4419-b03d-38f1d6beda3b","resolution":{"observed_at":"2026-08-15T15:57:48.112890Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.281642Z","title":null,"venue":null,"work_id":"e543bad8-8a1a-4d29-b7d7-dc4a9e8d30d5","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.811014Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:6e8efab5099fa94aebf1d7c87098c740c61c55c9d22353e76bd9790120e7be1d","observation_id":"dee26106-9ec9-4124-a598-50ebc4fc2e14","resolution":{"observed_at":"2026-08-15T15:57:48.284610Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.100362Z","title":null,"venue":null,"work_id":"2ab50b96-f361-4796-a963-45e360ec6b91","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.813409Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:5c3be1a25d70c7633fc312770ec7c16ccec83985b4ae9d486ab338867849130d","observation_id":"983cd72b-9aa0-4872-b136-b352e8d8e454","resolution":{"observed_at":"2026-08-15T15:57:48.103502Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.092781Z","title":"KNN-Precision","venue":null,"work_id":"bef8886d-e119-4ae1-9e7a-704952627bc0","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.816110Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:4a5553691c9b69cbd7c34b8b349c870141a07fb30eebd29b40f3904903ec420f","observation_id":"2d0c25f6-194d-476a-836e-c7dfa894d9ed","resolution":{"observed_at":"2026-08-15T15:57:48.095654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.082778Z","title":"topic prediction","venue":null,"work_id":"22e0769c-12a9-48fd-a464-6d9ee54c3b62","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.818769Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:17e01da9a133aa5ae3832fb9c3e673d8e201b67f7b928edc02ebf48bd59099dc","observation_id":"91de19a3-5f2b-4311-8ee3-397f1f65061e","resolution":{"observed_at":"2026-08-15T15:57:48.085415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T15:57:48.074190Z","title":"HUMAN:␣\", and ChatGPT response must start with","venue":null,"work_id":"57174923-bebd-4c82-bbfc-7fef9f6962da","year":null},"citing_paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-15T15:57:47.821635Z"},"links":{"citing_paper":"/paper/2509.10696"},"observation_digest":"sha256:6062c136de0c9396f2df9d471e558422f3e5ecb0b82009170518235b0beb3b47","observation_id":"ef23eeeb-9505-4ee9-8383-53d824118844","resolution":{"observed_at":"2026-08-15T15:57:48.076942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.10696","last_updated":"2025-09-12T21:18:13Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T14:50:00.678248Z","submitted_at":"2025-09-12T21:18:13Z","title":"Struct-Bench: A Benchmark for Differentially Private Structured Text Generation"},"reference_resolution":{"displayed":94,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":6,"unresolved":63,"verified_exact":2,"verified_fuzzy":23},"total_outbound_references":94},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 1 inbound Pith citation observation for arXiv:2509.10696."}