{"as_of":"2026-08-11T07:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:38a46ec4527ff78947963a034d6170b563536ab06670a9316fc3066019b90d65","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T23:01:04.371165Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2501.00169/citation-record","integrity":"/paper/2501.00169/integrity","json":"/paper/2501.00169/citation-record.json","paper":"/paper/2501.00169"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T23:01:04.716006Z","title":"O’Reilly Media, Inc","venue":null,"work_id":"671f54ea-7e68-440b-8b83-e17b93af79c0","year":2024},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.268299Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:76f3da2cff2b573f9ab8cc1b54cf7971be7f1ec39d335bff8e735226fda70ede","observation_id":"c5bdcdfa-61a4-493f-b84d-30383096990f","resolution":{"observed_at":"2026-08-10T23:01:04.720909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.698792Z","title":null,"venue":null,"work_id":"d1d3d004-d2c9-4b2f-ad53-c299bc6262df","year":2023},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.273830Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:4c92f2d6438d128eac361e374a42cdb627b4070d4ecf85671da50cadc944a8fb","observation_id":"fdf9d4e0-8eec-45e1-838e-8e7def0d0ef7","resolution":{"observed_at":"2026-08-10T23:01:04.704945Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.683343Z","title":"Brown and D","venue":null,"work_id":"2d524853-b41d-4320-8cba-24fceb07a3b8","year":1995},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.279094Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:dc63e34e3c62f5da8e1e26f56e4b723d6b448d8a35928d7dbf29183b71fb67d7","observation_id":"d7afe2b2-3f7e-4002-b64d-29737013b471","resolution":{"observed_at":"2026-08-10T23:01:04.688085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.665955Z","title":"Chollet and F","venue":null,"work_id":"77d76534-6c08-4dd1-9c4e-ef2a3c56f33b","year":2024},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.284965Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:62bd6d1fd6ea2d16f0c9d9f6f361592f4185a237e5b29076bc9718b1f4dee895","observation_id":"7489d3db-1506-4a1a-9c70-48298bccde2f","resolution":{"observed_at":"2026-08-10T23:01:04.672046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.649018Z","title":"Di Cosmo and D","venue":null,"work_id":"61fa4d0e-c701-4b13-a318-bf5742c7ff3c","year":2019},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.290968Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:c8ed06f0c4bd8537fc7980d3d52723584274d323ef3b20a3bb1624433d5705c6","observation_id":"811994d5-2c80-4c25-a5fc-9e999d3559b5","resolution":{"observed_at":"2026-08-10T23:01:04.654099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.633375Z","title":null,"venue":null,"work_id":"25d4f31e-8d95-42df-b278-14f57f20e104","year":1987},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.296441Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:342867c1041384d9205b933c8397899c9220562cffd57d89044e433d9c46a683","observation_id":"ddc9e9b7-7d39-4cc4-acac-9eb5b7fed1a3","resolution":{"observed_at":"2026-08-10T23:01:04.638179Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.617081Z","title":null,"venue":null,"work_id":"9576d112-3413-4f82-ae5f-8183ae9764b8","year":1995},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.303015Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:2b300600b99408c7efc1eebbf04ea937fb94f42c8b8fb32f78bffe01d6e719ba","observation_id":"01dc5ec9-b321-44b4-a302-db316a620818","resolution":{"observed_at":"2026-08-10T23:01:04.622415Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.599901Z","title":"Goodfellow, Y","venue":null,"work_id":"a52d2820-1fe9-4620-b81b-9e5328256a9d","year":2016},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.308161Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:7ace6e37a8207d5419f5184e24f632505edba59b8e3b17c4bf76b4880a647793","observation_id":"08d8402d-70cb-4916-bddf-e2fb3939d0d8","resolution":{"observed_at":"2026-08-10T23:01:04.604892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.583947Z","title":"Howard and S","venue":null,"work_id":"fd3f44fc-57c7-4fcf-9215-b61dcb1fea6c","year":2020},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.313238Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:b1659a52ac752cc6dd21d372d02d3d29e9160413fc0fcb86b6949af827db8274","observation_id":"815a445f-f65a-4490-bb2b-ba725a3e563c","resolution":{"observed_at":"2026-08-10T23:01:04.588877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.318253Z","title":"LeCun, Y","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.318253Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:a07cf38f7bf128e28ed699e8ba2ba021383bddb0e5696fe94b938510c8d01949","observation_id":"3c06cb25-b4f1-40b6-b6d8-7331bd328b9e","resolution":{"observed_at":"2026-08-10T23:01:04.318253Z","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":"2026-08-10T23:01:04.556466Z","title":"Martí-Oliet and J","venue":null,"work_id":"aba87be5-6a39-44d4-acf3-7befc582c1dc","year":1989},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.323585Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:456a5abdbc79abe9000713b0e3f1ef9c2819e0b71c3f5741eebc045f79fafaa5","observation_id":"5ef0d6e0-70b2-46da-bc73-851ce8abc17a","resolution":{"observed_at":"2026-08-10T23:01:04.561317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.540097Z","title":null,"venue":null,"work_id":"a8e0cfc0-f17f-4fba-92c5-a651f44d07fb","year":1977},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.328565Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:02b6375be923fc35c747bd847830866dac0636e40a8b736c21f5ee00db246733","observation_id":"a1363c1b-b421-4f96-8dbe-33f3cca3c2b5","resolution":{"observed_at":"2026-08-10T23:01:04.544867Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.524273Z","title":null,"venue":null,"work_id":"e24f931e-e859-43bf-87ec-22a67700879f","year":2013},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.334216Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:e53341fb0b8f8d1450072683538eeb626d6e4f9c5e9c8c59c402ef0eb0ddbc1d","observation_id":"4fd456ce-9139-400a-8f1d-2518c68da385","resolution":{"observed_at":"2026-08-10T23:01:04.529038Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.506523Z","title":"Salvagno, F","venue":null,"work_id":"db961b15-ff6a-4991-9840-fd7888627b6d","year":2023},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.339547Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:77c71fe9dfa660d304bce42fa0cda0ef0933ef836698c6768139d7211aa67722","observation_id":"1f57efd9-1396-4b86-ac76-013ad1e4ae73","resolution":{"observed_at":"2026-08-10T23:01:04.512265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.488566Z","title":"O’Reilly Media, Inc","venue":null,"work_id":"dc99a461-9966-4cc6-b8df-dc5f34fe9efe","year":2024},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.344763Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:3c57d55b403e0a7239be54e322c1d013dacf1fb2a8caa7768117d1a73b7500cf","observation_id":"762a5baa-9e2b-4420-ae2d-bf17151e494f","resolution":{"observed_at":"2026-08-10T23:01:04.494411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.471349Z","title":null,"venue":null,"work_id":"9bc93c4c-348b-41b6-b8dc-6f682246847d","year":1993},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.349689Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:4d26c480493345c54eae4298a14d488e9e4a4737526f7460e5a874335504634b","observation_id":"3bf1484c-1162-4329-a964-73da68e13f28","resolution":{"observed_at":"2026-08-10T23:01:04.476665Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.454120Z","title":"Schack-Nielsen and C","venue":null,"work_id":"70bebe6a-9425-4eb8-8e0d-765df2e521c7","year":2008},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.355175Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:8f3421b9898caa2526cb7e689082cabd3a7bf0875d826d2e26363d07bfdb3e31","observation_id":"bdf84160-1135-4b15-9cea-ad5126d61e1c","resolution":{"observed_at":"2026-08-10T23:01:04.459061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-08-10T23:01:04.438703Z","title":"Stevens, L","venue":null,"work_id":"2c133b38-438a-4705-b5db-ef90306cd985","year":2020},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.360128Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:98ba7a40f1f2b2b7b3d193871a8a7951c98b6e881ee49e73431fff44daf14379","observation_id":"b6c66241-5491-41cb-a5ff-5c1659816123","resolution":{"observed_at":"2026-08-10T23:01:04.443631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.10701","last_updated":"2019-10-22T06:07:55Z","snapshot_observed_at":"2026-08-03T06:07:39.760319Z","submitted_at":"2019-07-24T20:18:28Z","title":"Benchmarking TPU, GPU, and CPU Platforms for Deep Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.10701","snapshot_observed_at":"2026-08-10T23:01:04.365156Z","title":null,"venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.365156Z"},"links":{"cited_paper":"/paper/1907.10701","citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:da9bfbc3bdd031afeec228fdda7875205fcf8a3c1880e39fdfe32d5425f7dd00","observation_id":"06f7c554-ae9d-429d-8c38-06ed84e54a38","resolution":{"observed_at":"2026-08-10T23:01:04.365156Z","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":"2026-08-10T23:01:04.420643Z","title":"Watkins, I","venue":null,"work_id":"57be0952-037d-4ba4-9b5e-21dc456a543c","year":2003},"citing_paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T23:01:04.371165Z"},"links":{"citing_paper":"/paper/2501.00169"},"observation_digest":"sha256:426c518fdb37732300213d6b48e86043940e929824aadb0b306e496db377290f","observation_id":"f19ca2ac-e061-4653-b052-033b0c51e896","resolution":{"observed_at":"2026-08-10T23:01:04.427348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.00169","last_updated":"2024-12-30T22:38:56Z","latest_version":1,"primary_category":"cs.PL","snapshot_observed_at":"2026-08-10T22:55:29.682731Z","submitted_at":"2024-12-30T22:38:56Z","title":"DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":20},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2501.00169."}