{"as_of":"2026-08-11T12:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a8835d99ef532aebb3ecdacf8a983ff4d70d41c3ad2e3a877340294b826450f9","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":"2026-08-11T11:57:20.646926Z","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:a04a4b2e2a8b5cacdb3520d324f3bfb0b5a3d0702dc4c81ebf75bea475a9b7fe","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":"2026-08-11T11:57:20.646926Z","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:c27ad98f5c63f2bc7beaabf037cd9baa0425161b2f437eb0beffbd801921b46e","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":"2026-08-11T11:57:20.646926Z","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":"sha256:15f099ba8a9f0c35bcdd37ce91c0633833e7ac2577f8fb7e9eccf3342cc9d143","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":"2026-08-11T11:57:20.646926Z","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":"sha256:3ae8b7321a6b150978f3a5921583f6b8550d4d6b179caa4a45238867d5f69a61","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":"2026-08-11T11:57:20.646926Z","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:cf10733d13983c22bdedbb651965371f9a9baec31b4d462abefebc92e436dee5","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":"2026-08-11T11:57:20.646926Z","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":"sha256:04cb3d282a046cc663a18f7106b8475f021be7a6ca3d12b0bb87d2f315c97b18","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":"2026-08-11T11:57:20.646926Z","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":"sha256:7041996bc7c9196672f782990dde7adb1a38440b7c2eeaef0255eff15533367c","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":"2026-08-11T11:57:20.646926Z","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":"sha256:9cf24e6c3bab997f63960ff3db4636aab6ac0607c362493b0c249951c970269e","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":"2026-08-11T11:57:20.646926Z","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":"sha256:1b733e06b3f166d0e6aca0ac279cd1fd0d1199f39c81b02ef3c8593349ab4575","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":"2026-08-11T11:57:20.646926Z","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":"sha256:9d0e0ef851ebecf4e82a36fd94195fa934f85add866c86eba4dca325c35eade0","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":"2026-08-11T11:57:20.646926Z","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":"sha256:7fc906e15a764366b4b68d652178e95339cb0863dcc1a425386c00b255f42171","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":"2026-08-11T11:57:20.646926Z","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":"sha256:96687e0e516f40b888a610ff653a094c6eacc219cd219d80bdfaafb5a071a1fd","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":"2026-08-11T11:57:20.646926Z","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":"sha256:8448158fc9a9403eb230e24f8a8e7e1ca3b774c694fc108851cfe9ea5075b394","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":"2026-08-11T11:57:20.646926Z","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:ac315544e2b7066b8947e0703c05761ff4fb86a487115223507ec3f34a4f07a7","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":"2026-08-11T11:57:20.646926Z","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:c5409a39db449a985b234f31609fa181b7e5465f91a36327acc9f34c54153feb","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":"2026-08-11T11:57:20.646926Z","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":"sha256:37c7a90074b01ca42bae45326bbf2f56c6b90aeadc9809f6f6a1fddc7edbcc09","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":"2026-08-11T11:57:20.646926Z","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:a457281fdc9fb578d0f21500c8e559e90889ceb1b0c340a184c22ad36a1340c7","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":"2026-08-11T11:57:20.646926Z","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:c33d952099bb61da363470421b2d09b6d5a5ebd7c054335b02cf493bc516187a","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":"2026-08-11T11:57:20.646926Z","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:c62d5b4b6c447dd5f6ffe436b8502ea8054c82528523be2290578a65f6a0dd9a","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":"2026-08-11T11:57:20.646926Z","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:eb6382463a614d728425e8b2b9e1d8d2e595a94ef32c4b6ec5e9831115adaa7c","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":"2026-08-11T11:57:20.646926Z","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."}