{"as_of":"2026-08-08T13:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5c853ccb6efe223076b732df1b3431fb3a5d7a6408a5801c2cb99ed73ff5c99c","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:42:25.981495Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T17:08:43.973653Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2102.09407","last_updated":"2024-03-16T12:40:45Z","snapshot_observed_at":"2026-07-06T10:42:32.217922Z","submitted_at":"2021-02-18T14:53:12Z","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.09407","snapshot_observed_at":"2026-08-06T17:42:25.981495Z","title":"Adaptive rational activations to boost deep reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.10637","last_updated":"2025-07-18T08:54:22Z","snapshot_observed_at":"2026-08-06T17:33:19.566531Z","submitted_at":"2025-07-14T13:18:26Z","title":"A Simple Baseline for Stable and Plastic Neural Networks","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T17:42:25.981495Z"},"links":{"cited_paper":"/paper/2102.09407","citing_paper":"/paper/2507.10637"},"observation_digest":"sha256:7f621def64b83e8185e8e70d04f2cccd848f679e46f6346dc6a03273c1ff8413","observation_id":"5103e1ec-13b2-476c-8558-7d46e14c6eb8","resolution":{"observed_at":"2026-08-06T17:42:25.981495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.09407","last_updated":"2024-03-16T12:40:45Z","snapshot_observed_at":"2026-07-06T10:42:32.217922Z","submitted_at":"2021-02-18T14:53:12Z","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","version":5},"cited_work":{"arxiv_id":"2102.09407","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.09407","snapshot_observed_at":"2026-07-03T17:08:43.973653Z","title":"Recurrent rational networks.arXiv preprint arXiv:2102.09407, 2021a","venue":null,"work_id":"dc1c8b8b-4125-4d05-939a-1f2bdab65749","year":null},"citing_paper":{"arxiv_id":"2509.22562","last_updated":"2026-04-30T15:40:29Z","snapshot_observed_at":"2026-07-06T22:30:55.313733Z","submitted_at":"2025-09-26T16:41:47Z","title":"Activation Function Design Sustains Plasticity in Continual Learning","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-18T13:00:27.749673Z"},"links":{"cited_paper":"/paper/2102.09407","citing_paper":"/paper/2509.22562"},"observation_digest":"sha256:a3ef3594fb125075a42da2343d5dddb07c291bdb41055ebd0665d5b94333fd35","observation_id":"ee5aa70b-0af9-4596-a9be-37ac28981dc3","resolution":{"observed_at":"2026-05-18T13:01:23.599848Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.09407","last_updated":"2024-03-16T12:40:45Z","snapshot_observed_at":"2026-07-06T10:42:32.217922Z","submitted_at":"2021-02-18T14:53:12Z","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","version":5},"cited_work":{"arxiv_id":"2102.09407","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.09407","snapshot_observed_at":"2026-07-03T17:08:43.973653Z","title":"Recurrent rational networks.arXiv preprint arXiv:2102.09407, 2021a","venue":null,"work_id":"dc1c8b8b-4125-4d05-939a-1f2bdab65749","year":null},"citing_paper":{"arxiv_id":"2604.15414","last_updated":"2026-06-09T00:58:37Z","snapshot_observed_at":"2026-07-12T19:46:39.068858Z","submitted_at":"2026-04-16T17:06:54Z","title":"Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T11:01:17.325738Z"},"links":{"cited_paper":"/paper/2102.09407","citing_paper":"/paper/2604.15414"},"observation_digest":"sha256:85ff4bbde8601ce3a9d26751771b02137e75fa371933c8efbce68b587a757206","observation_id":"7e3ec0ea-1a22-4cad-be7d-38126e9a99dd","resolution":{"observed_at":"2026-05-10T11:05:08.922723Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.09407","last_updated":"2024-03-16T12:40:45Z","snapshot_observed_at":"2026-07-06T10:42:32.217922Z","submitted_at":"2021-02-18T14:53:12Z","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.09407","snapshot_observed_at":"2026-07-12T19:46:39.624903Z","title":"Adaptive rational activations to boost deep reinforcement learning.arXiv preprint arXiv:2102.09407,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.15414","last_updated":"2026-06-09T00:58:37Z","snapshot_observed_at":"2026-07-12T19:46:39.068858Z","submitted_at":"2026-04-16T17:06:54Z","title":"Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-12T19:46:39.624903Z"},"links":{"cited_paper":"/paper/2102.09407","citing_paper":"/paper/2604.15414"},"observation_digest":"sha256:46beb75685d08fe3713f1f61635668066636762e179cf6d5a15a20e4f3b38136","observation_id":"36e7c68b-e188-4b7f-91fd-ef063c510d02","resolution":{"observed_at":"2026-07-12T19:46:39.624903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.09407","last_updated":"2024-03-16T12:40:45Z","snapshot_observed_at":"2026-07-06T10:42:32.217922Z","submitted_at":"2021-02-18T14:53:12Z","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","version":5},"cited_work":{"arxiv_id":"2102.09407","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2102.09407","snapshot_observed_at":"2026-07-03T17:08:43.973653Z","title":"Recurrent rational networks.arXiv preprint arXiv:2102.09407, 2021a","venue":null,"work_id":"dc1c8b8b-4125-4d05-939a-1f2bdab65749","year":null},"citing_paper":{"arxiv_id":"2606.14990","last_updated":"2026-06-16T02:02:24Z","snapshot_observed_at":"2026-08-01T23:42:27.019491Z","submitted_at":"2026-06-12T22:22:40Z","title":"Rational Sparse Autoencoder","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T04:29:27.383661Z"},"links":{"cited_paper":"/paper/2102.09407","citing_paper":"/paper/2606.14990"},"observation_digest":"sha256:d63f08a27a5802d9173fa166c623a61bf76da0738e767b69297fc4ce17be9780","observation_id":"9cf50272-b609-4bca-84de-75f626569c15","resolution":{"observed_at":"2026-07-03T17:08:43.975197Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2102.09407/citation-record","integrity":"/paper/2102.09407/integrity","json":"/paper/2102.09407/citation-record.json","paper":"/paper/2102.09407"},"outbound":[],"paper":{"arxiv_id":"2102.09407","last_updated":"2024-03-16T12:40:45Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T10:42:32.217922Z","submitted_at":"2021-02-18T14:53:12Z","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2102.09407."}