{"as_of":"2026-08-10T00:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b9f7bea972f364514514580cb4e305029c72279c06666405699b14fa0216ed1","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T11:56:41.412309Z","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":"2026-07-02T12:06:55.523850Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.04461","last_updated":"2025-06-16T21:12:08Z","snapshot_observed_at":"2026-07-06T19:28:35.110264Z","submitted_at":"2024-10-06T12:22:32Z","title":"Improved Off-policy Reinforcement Learning in Biological Sequence Design","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04461","snapshot_observed_at":"2026-08-08T11:56:41.412309Z","title":"Improved off-policy reinforcement learning in biological sequence design","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07735","last_updated":"2025-09-11T14:32:19Z","snapshot_observed_at":"2026-08-08T11:42:32.276078Z","submitted_at":"2025-02-11T17:55:03Z","title":"Revisiting Non-Acyclic GFlowNets in Discrete Environments","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T11:56:41.412309Z"},"links":{"cited_paper":"/paper/2410.04461","citing_paper":"/paper/2502.07735"},"observation_digest":"sha256:61819f1a34856adaa62745c72a40b6baa7ca7fdff78a8489c3f9027f4a9f4abf","observation_id":"990cbd0b-1dd2-4191-a8e9-f9a6f21af3fe","resolution":{"observed_at":"2026-08-08T11:56:41.412309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04461","last_updated":"2025-06-16T21:12:08Z","snapshot_observed_at":"2026-07-06T19:28:35.110264Z","submitted_at":"2024-10-06T12:22:32Z","title":"Improved Off-policy Reinforcement Learning in Biological Sequence Design","version":2},"cited_work":{"arxiv_id":"2410.04461","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.04461","snapshot_observed_at":"2026-07-02T12:06:55.523850Z","title":"arXiv preprint arXiv:2410.04461 , year=","venue":null,"work_id":"4df38edd-8d8c-4816-b597-e9e7f6f8af86","year":2024},"citing_paper":{"arxiv_id":"2605.26690","last_updated":"2026-05-26T08:29:36Z","snapshot_observed_at":"2026-08-08T09:09:25.824201Z","submitted_at":"2026-05-26T08:29:36Z","title":"Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T19:21:13.698546Z"},"links":{"cited_paper":"/paper/2410.04461","citing_paper":"/paper/2605.26690"},"observation_digest":"sha256:902625a057967f77d5d68d566ebc2a6eb54948923526a4dbe3697baea953e787","observation_id":"989d4356-ac34-4638-ab16-c5537352265b","resolution":{"observed_at":"2026-06-29T19:23:53.813400Z","resolver_source":"arxiv_id","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":"2410.04461","last_updated":"2025-06-16T21:12:08Z","snapshot_observed_at":"2026-07-06T19:28:35.110264Z","submitted_at":"2024-10-06T12:22:32Z","title":"Improved Off-policy Reinforcement Learning in Biological Sequence Design","version":2},"cited_work":{"arxiv_id":"2410.04461","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.04461","snapshot_observed_at":"2026-07-02T12:06:55.523850Z","title":"arXiv preprint arXiv:2410.04461 , year=","venue":null,"work_id":"4df38edd-8d8c-4816-b597-e9e7f6f8af86","year":2024},"citing_paper":{"arxiv_id":"2606.06272","last_updated":"2026-06-04T15:14:24Z","snapshot_observed_at":"2026-08-05T16:40:32.420875Z","submitted_at":"2026-06-04T15:14:24Z","title":"Your GFlowNet Secretly Learns an Optimal Transport Plan","version":1},"reference_index":150,"source":"arxiv_source","source_observed_at":"2026-06-28T02:35:08.323804Z"},"links":{"cited_paper":"/paper/2410.04461","citing_paper":"/paper/2606.06272"},"observation_digest":"sha256:c435392880d96b116e15e5982a90ecc518243a958f1c094a672da55e3798aea4","observation_id":"8d8d3729-4187-4a24-86e6-0d4c8abdee7f","resolution":{"observed_at":"2026-07-02T12:06:55.525141Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2410.04461/citation-record","integrity":"/paper/2410.04461/integrity","json":"/paper/2410.04461/citation-record.json","paper":"/paper/2410.04461"},"outbound":[],"paper":{"arxiv_id":"2410.04461","last_updated":"2025-06-16T21:12:08Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T19:28:35.110264Z","submitted_at":"2024-10-06T12:22:32Z","title":"Improved Off-policy Reinforcement Learning in Biological Sequence Design"},"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":"2026-08-09T06:31:02.800959+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 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.04461."}