{"as_of":"2026-08-10T06:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b6d951e9dd8095c235372772831f37a3c2dfbc219aa06c7adcd81c55f7c6b44","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:05:52.584018Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.11821/citation-record","integrity":"/paper/2507.11821/integrity","json":"/paper/2507.11821/citation-record.json","paper":"/paper/2507.11821"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:05:52.510037Z","title":"Gradient-based learning applied to document recognition,","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.510037Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:80c005f9c77288540fcef63d784bb2311e25e55b1ddc497abd935c7b1611727e","observation_id":"647d8e5c-fc93-4dbb-8968-223f1cac2f85","resolution":{"observed_at":"2026-08-06T17:05:52.510037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07747","last_updated":"2017-09-15T21:29:49Z","snapshot_observed_at":"2026-07-06T05:56:41.814255Z","submitted_at":"2017-08-25T14:01:29Z","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07747","snapshot_observed_at":"2026-08-06T17:05:52.513338Z","title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.513338Z"},"links":{"cited_paper":"/paper/1708.07747","citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:de2b4d7622606b9d0ee46f1df18b05e001468b0a6b0785df4dfeea504da7e5b6","observation_id":"6c6657fd-c339-418c-bf31-8f235f8df38c","resolution":{"observed_at":"2026-08-06T17:05:52.513338Z","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":"2026-08-06T17:05:52.961632Z","title":"Revisiting unreasonable effectiveness of data in deep learning era,","venue":null,"work_id":"e2fb0438-3671-4b41-9dd9-2b7029f05bd3","year":2017},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.516264Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:cd0e8efec7b350cb5c5a47f5c4f0c00fcd3b9570e14c9b30ab81dd5290bfc87e","observation_id":"56e3f093-4faa-432a-a5b0-7dcb669d4351","resolution":{"observed_at":"2026-08-06T17:05:52.964141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.953759Z","title":"Food-101–mining dis- criminative components with random forests,","venue":null,"work_id":"8ff3d41a-2b89-4fa3-911c-e1ed8db1f482","year":2014},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.518851Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:0b020bed498a1a44242eb04c9f8925e2d6c68e04ea6b74e0caf0a8e8d6ee542b","observation_id":"8c38be9e-ba7e-4fe1-be06-6ebbb45cd69b","resolution":{"observed_at":"2026-08-06T17:05:52.956589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.946468Z","title":"Fine-grained plant classification using convolutional neural networks for feature extraction","venue":null,"work_id":"89c9ddeb-13bc-413a-a69a-93a69dc2cce1","year":2014},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.521624Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:1f301223c4ef2973dca37617a3cdc845329d65615a77cdbb009ed0c13fe7d166","observation_id":"8a6c15e7-a4ab-43c3-8b34-ff67e60ac44c","resolution":{"observed_at":"2026-08-06T17:05:52.948932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11416","last_updated":"2025-05-16T16:29:19Z","snapshot_observed_at":"2026-08-07T15:44:35.852824Z","submitted_at":"2025-05-16T16:29:19Z","title":"MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection","version":1},"cited_work":{"arxiv_id":"2505.11416","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.11416","snapshot_observed_at":"2026-08-06T17:05:52.770925Z","title":"MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection","venue":"cs.NE","work_id":"9c60d394-ec5a-49b0-85e2-f7ea4da2783f","year":2025},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.524197Z"},"links":{"cited_paper":"/paper/2505.11416","citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:a61da76c0b3e9792250dd05cafd3f53501168954e5388ad9db4a5c0f199aded5","observation_id":"9c396ffe-aa18-456e-9fc9-30ac4daeefcd","resolution":{"observed_at":"2026-08-06T17:05:52.774100Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.939313Z","title":"Learning visual features from large weakly supervised data,","venue":null,"work_id":"b9a647f2-c533-4d8d-8812-588301ac03be","year":2016},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.527125Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:323a392042cc501bd3c0bb5863d1052ef116590e0d124042b669658910065cc6","observation_id":"33ffc3f5-c4f0-469f-bb7a-631233233a3c","resolution":{"observed_at":"2026-08-06T17:05:52.941817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.931581Z","title":"A semi-supervised fake news detection using sentiment encoding and lstm with self-attention,","venue":null,"work_id":"115c544c-d920-4e35-a0e3-d9ed51deb1f8","year":2023},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.529481Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:8950cbe6578c4bf679504e3144aa56f4a37b68fb2b4fffa8c1edc449ac4c04eb","observation_id":"40cc66c9-b529-4509-9b6f-c76405c7c404","resolution":{"observed_at":"2026-08-06T17:05:52.934235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.923865Z","title":"A new chebyshev operational matrix formulation of least-squares support vector regression for solving fractional integro-differential equations,","venue":null,"work_id":"efab2ac0-5a28-4a40-b4d3-e793dfcaa46b","year":2025},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.531971Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:b349fdc753b07b5261005e6dc81462bf71d7e9f5ecaaf21affcbc89b94f7a55d","observation_id":"210be689-e255-4a44-96d9-64bdfdf24c9f","resolution":{"observed_at":"2026-08-06T17:05:52.926674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.916369Z","title":"A machine learning framework for efficiently solving fokker–planck equations,","venue":null,"work_id":"a25e22b6-4f65-4a4d-a5a6-bee7d458b08a","year":2024},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.534435Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:5fdbf8f8552a31416a13aa2f27ac32d9167b3dcd011a9636a5b5829755fdf9bc","observation_id":"31349af9-8d7f-485c-8a5e-1b70ac9e67ea","resolution":{"observed_at":"2026-08-06T17:05:52.918985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.908840Z","title":"Emnist: Extending mnist to handwritten letters,","venue":null,"work_id":"d2ee90d8-99ab-44ed-a70e-400e213cdc3e","year":2017},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.536731Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:920b81d7109eb9134b381ce7a9fda772e8cfafab66631bff7a288b1b819ae957","observation_id":"44d82cc2-ed2a-432d-b321-828d8eb2df6c","resolution":{"observed_at":"2026-08-06T17:05:52.911325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2504.18837","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:05:52.758530Z","title":"Sentiment and social signals in the climate crisis: A survey on analyzing social media responses to extreme weather events,","venue":null,"work_id":"d465ca33-c955-41e0-9fd3-f0719eaa210b","year":2025},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.539093Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:b8afd66db71d0e4fc37a3048196e3bd180de2ac23bf6771b650ea22ac3ff2429","observation_id":"50a4ac2d-b8b0-4a94-8306-94908889d053","resolution":{"observed_at":"2026-08-06T17:05:52.762842Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04616","last_updated":"2025-02-20T21:34:03Z","snapshot_observed_at":"2026-07-06T19:28:43.743447Z","submitted_at":"2024-10-06T20:33:22Z","title":"Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?","version":2},"cited_work":{"arxiv_id":"2410.04616","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.04616","snapshot_observed_at":"2026-08-06T17:05:52.630398Z","title":"Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?","venue":"cs.CL","work_id":"45b35e5d-2a75-431b-9039-b28784ffc418","year":2024},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.541556Z"},"links":{"cited_paper":"/paper/2410.04616","citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:da132ebb7714bd5e4456bae55c14f6ca164dbaf84a484f3fb8d376417c0831df","observation_id":"8a4e5e74-d8f9-4f9d-a20e-847d616b4f34","resolution":{"observed_at":"2026-08-06T17:05:52.633566Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.901203Z","title":"A multimodal physics-informed neural network approach for mean radiant temperature modeling,","venue":null,"work_id":"b350ae64-b449-47d6-a32d-bb66c7ef099c","year":null},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.544245Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:190c493220f01258bd0b0b0fa9b80dcd998a8ff84efa9f2e1bae7e20de3bfc97","observation_id":"47b9b66d-884e-45e7-99b5-2994e611bdb0","resolution":{"observed_at":"2026-08-06T17:05:52.904022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.893501Z","title":"Webmrt: An online tool to predict summertime mean radiant tempera- ture using machine learning,","venue":null,"work_id":"6433c3b5-9f36-4ce8-8c59-7f495f4b0971","year":2024},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.549689Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:36899aa713a3b980e336cc61b8c8d564d9f0f1e08080e0ad584aeb31b01e9c4d","observation_id":"667d6aef-ee68-4c31-89e6-99ab0a639029","resolution":{"observed_at":"2026-08-06T17:05:52.896483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.885862Z","title":"Urban form and composition of street canyons: A human-centric big data and deep learning approach,","venue":null,"work_id":"c4ea417e-83c9-4bbd-a958-2a81c0be685c","year":2019},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.551927Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:b6e34469d0a14302acabfc114808e12b532e6156c5ad904b53bdd722c0a1543f","observation_id":"ad74a0f2-0970-4742-a471-cd61ddd4250c","resolution":{"observed_at":"2026-08-06T17:05:52.888633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.877717Z","title":"Snorkel: Rapid training data creation with weak supervision,","venue":null,"work_id":"0609a520-54cd-4250-bad9-9af151b3885d","year":2020},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.554153Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:81bf8259bd7b2db39fa62db52bbecd2ef4dab5b3414eae25cdd46bef8ad59ff5","observation_id":"77c5f577-fdef-4ecd-8974-936da325e66e","resolution":{"observed_at":"2026-08-06T17:05:52.880401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.869986Z","title":"Label studio: Open-source data labeling tool,","venue":null,"work_id":"3e1f6059-8ed1-45b1-9216-f79ed9775e32","year":2020},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.556350Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:0d2bd993d21651c6e88016767e390d325f13e6770ee910ca84eb415fcbe8e786","observation_id":"467ee9ad-ca45-4f58-851c-a5a4fa413bc3","resolution":{"observed_at":"2026-08-06T17:05:52.872597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.862471Z","title":"Google automl: cloud vision,","venue":null,"work_id":"49984002-0a1f-442e-abda-6baadb0d4f68","year":2019},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.558680Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:85bfb6fb342fa9e682d2cb6315f19c6e0b0786999ae01004df1174b42f269e1e","observation_id":"bf7b007c-605f-4ca6-8f2a-1af00403c26b","resolution":{"observed_at":"2026-08-06T17:05:52.865014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.855096Z","title":"Nvidia tao toolkit,","venue":null,"work_id":"ba7863c8-31cb-49be-a5ee-5263cbdca91d","year":2025},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.561107Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:822b84aa7c52d79147e727e6da2fa9edc3450697e52839b664b3f7e4610ea622","observation_id":"f09182c3-7299-4980-ad91-ae7b8b2c9d7b","resolution":{"observed_at":"2026-08-06T17:05:52.857579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.847405Z","title":"A survey of data collection methods for machine learning,","venue":null,"work_id":"22a31f99-c53e-436d-891c-3268e64a30d1","year":2021},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.563410Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:86e7a658761ab0e6325c44b9d798a7bf1676b0ea4012f32be61f47e337125b34","observation_id":"691878f3-f26d-41cf-b787-d9c0e7b2d1b4","resolution":{"observed_at":"2026-08-06T17:05:52.850038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17591","last_updated":"2024-07-21T10:35:41Z","snapshot_observed_at":"2026-08-08T13:46:20.002563Z","submitted_at":"2024-06-25T14:32:31Z","title":"DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation","version":3},"cited_work":{"arxiv_id":"2406.17591","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.17591","snapshot_observed_at":"2026-08-06T17:05:52.608843Z","title":"DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document Annotation","venue":"cs.CV","work_id":"90b0a401-c1d8-4ce0-8292-c3d36d8e13da","year":2024},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.565642Z"},"links":{"cited_paper":"/paper/2406.17591","citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:dcf6052a1cd8ef1b322b322b0a747e7a5c04ff87cf6db70136f67ef5d263ae40","observation_id":"8555375e-12c2-4d15-9e98-24c14e00ceb6","resolution":{"observed_at":"2026-08-06T17:05:52.613534Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.568211Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.568211Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:6d9e5bfd1ab5a88c77ae36925522cc2dc7e836e9a5903aeb2e50730e9703b04e","observation_id":"aeda628b-aefb-4254-be88-cf8cc31a58e0","resolution":{"observed_at":"2026-08-06T17:05:52.568211Z","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":"2026-08-06T17:05:52.834941Z","title":"Fong and D","venue":null,"work_id":"070d254f-17b3-4ba2-bd6f-dac1ab4e6dee","year":2019},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.570275Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:5111f69660d2d4f70512a94bd57c1f00a29fa95d9943dcfe93aabc0803c5a432","observation_id":"0d1bfb69-2947-4ca8-87b8-9f14f4b1b9d6","resolution":{"observed_at":"2026-08-06T17:05:52.837532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.826678Z","title":"Level- k reasoning, deep rein- forcement learning, and monte carlo decision process for fast and safe automated lane change and speed management,","venue":null,"work_id":"9b970ec5-d413-472c-aec7-f84bd4735ef8","year":2023},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.572571Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:b0463653a141f607b073340a10d04faab88bd3bf43349bf4a8239557d8980621","observation_id":"f5e6ed47-89a8-404b-ac26-d9f9f6f9e23a","resolution":{"observed_at":"2026-08-06T17:05:52.829782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.818683Z","title":"Learning to reweight ex- amples for robust deep learning,","venue":null,"work_id":"76a42edc-260a-4763-a007-d61f46cc2513","year":2018},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.574886Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:3923fbe7bb080bdfdcfbef586d069175090da3a73d077b3928fa451ca8e74b98","observation_id":"75b2537f-8b9c-42f8-8718-6c3e38258df3","resolution":{"observed_at":"2026-08-06T17:05:52.821382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.810938Z","title":"Deliberate practice in data selection for efficient learning,","venue":null,"work_id":"385ca495-a862-4982-9b04-2aefd4a5947c","year":2021},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.577104Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:246c7fa7b8204d29f7ef6b869f8fba6df502b60571ef0299ba45c78d69126a88","observation_id":"b02e19e2-7850-47d7-a575-7f7f207a4472","resolution":{"observed_at":"2026-08-06T17:05:52.813619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.803598Z","title":"Adaptive data selection for labeling via reinforcement learning,","venue":null,"work_id":"68d50cf7-cb04-4fe5-b719-5663f50329e2","year":2023},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.579525Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:2a94b52d4e0476d5f6968f6dca14c0f27011f1ba8d4776b58cfb9943b5b555ed","observation_id":"bc6a687c-2cc1-45ab-aa64-a728a5ebb05a","resolution":{"observed_at":"2026-08-06T17:05:52.805984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.795709Z","title":"U2-net: Going deeper with nested u-structure for salient object detection,","venue":null,"work_id":"f60e4fb7-952a-4c29-9801-ed6fe94dde74","year":2020},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.581771Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:2ed404cea04313d1031cce480e2f64ca52a7e517ba5597da1a2e43c0bdc2686f","observation_id":"5821e71a-7176-48dd-a254-d61900f1125f","resolution":{"observed_at":"2026-08-06T17:05:52.798500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T17:05:52.787650Z","title":"Food/non-food image classifi- cation and food categorization using pre-trained googlenet model,","venue":null,"work_id":"e9a9c5d6-31e8-47c0-84b5-b09c69b83ffd","year":2016},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.584018Z"},"links":{"citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:7cdce5714f2606fa67222cea7e9ec9556fa29f9ea3e696b78a9b76f6f23992a0","observation_id":"c93a205f-2364-433d-82d1-4355f345a6de","resolution":{"observed_at":"2026-08-06T17:05:52.790441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.08482","last_updated":"2025-03-11T14:36:08Z","snapshot_observed_at":"2026-08-10T01:49:56.203350Z","submitted_at":"2025-03-11T14:36:08Z","title":"A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.08482","snapshot_observed_at":"2026-08-06T17:05:52.547006Z","title":"Available: https://arxiv.org/abs/2503.08482","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:52.547006Z"},"links":{"cited_paper":"/paper/2503.08482","citing_paper":"/paper/2507.11821"},"observation_digest":"sha256:33568c2f243eae7dec5ac655b7fab128f75bb2678229496914f82afc4bbba325","observation_id":"eff12eb5-1e63-4c16-bf79-8931d62a4292","resolution":{"observed_at":"2026-08-06T17:05:52.547006Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.11821","last_updated":"2025-07-16T00:50:09Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T08:03:20.924603Z","submitted_at":"2025-07-16T00:50:09Z","title":"MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":4,"verified_fuzzy":23},"total_outbound_references":31},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.11821."}