{"as_of":"2026-08-09T13:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ffd190bd6e853d7397deeae0749ac4508a905f8b534e3712a48f495e0cbd1846","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":"2026-08-08T18:56:45.271049Z","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":"2026-07-03T20:58:57.264863Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.13544","last_updated":"2020-11-29T03:18:13Z","snapshot_observed_at":"2026-08-09T05:50:47.015160Z","submitted_at":"2020-07-27T15:21:22Z","title":"Combining Deep Reinforcement Learning and Search for Imperfect-Information Games","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.13544","snapshot_observed_at":"2026-08-08T18:56:45.271049Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05555","last_updated":"2025-02-08T12:57:02Z","snapshot_observed_at":"2026-08-08T18:50:00.725926Z","submitted_at":"2025-02-08T12:57:02Z","title":"Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T18:56:45.271049Z"},"links":{"cited_paper":"/paper/2007.13544","citing_paper":"/paper/2502.05555"},"observation_digest":"sha256:ba7f7bb2aa414c26897c4a198be03376de86c3072c7f52a3b09a72ce149e621e","observation_id":"ae84acbf-5c05-42f1-97c3-5d9cec001d66","resolution":{"observed_at":"2026-08-08T18:56:45.271049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.13544","last_updated":"2020-11-29T03:18:13Z","snapshot_observed_at":"2026-08-09T05:50:47.015160Z","submitted_at":"2020-07-27T15:21:22Z","title":"Combining Deep Reinforcement Learning and Search for Imperfect-Information Games","version":2},"cited_work":{"arxiv_id":"2007.13544","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.13544","snapshot_observed_at":"2026-07-03T20:58:57.264863Z","title":"Combining Deep Rein- forcement Learning and Search for Imperfect-Information Games, November 2020","venue":null,"work_id":"89a463dc-1288-4b16-9081-da50d015b0ca","year":2020},"citing_paper":{"arxiv_id":"2606.29457","last_updated":"2026-06-28T15:21:27Z","snapshot_observed_at":"2026-08-04T08:01:40.222272Z","submitted_at":"2026-06-28T15:21:27Z","title":"How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T07:03:22.061105Z"},"links":{"cited_paper":"/paper/2007.13544","citing_paper":"/paper/2606.29457"},"observation_digest":"sha256:49d58ce601709f9c90410c1e0571d2f1307c0fee81a24b4d39be1a63dda5344f","observation_id":"e8c78199-6de8-44ff-82dd-4034fd956a69","resolution":{"observed_at":"2026-06-30T07:04:20.853307Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2007.13544","last_updated":"2020-11-29T03:18:13Z","snapshot_observed_at":"2026-08-09T05:50:47.015160Z","submitted_at":"2020-07-27T15:21:22Z","title":"Combining Deep Reinforcement Learning and Search for Imperfect-Information Games","version":2},"cited_work":{"arxiv_id":"2007.13544","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.13544","snapshot_observed_at":"2026-07-03T20:58:57.264863Z","title":"Combining Deep Rein- forcement Learning and Search for Imperfect-Information Games, November 2020","venue":null,"work_id":"89a463dc-1288-4b16-9081-da50d015b0ca","year":2020},"citing_paper":{"arxiv_id":"2607.01498","last_updated":"2026-07-01T21:56:51Z","snapshot_observed_at":"2026-08-02T18:41:43.276241Z","submitted_at":"2026-07-01T21:56:51Z","title":"Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-03T20:54:44.991267Z"},"links":{"cited_paper":"/paper/2007.13544","citing_paper":"/paper/2607.01498"},"observation_digest":"sha256:dfd1de8117931296f28435a965fd0c279a6e6f13a35e2b4cfe07a72c0589d0e8","observation_id":"00c6dfa2-744a-4d0c-a421-93f7705c925f","resolution":{"observed_at":"2026-07-03T20:58:57.266451Z","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/2007.13544/citation-record","integrity":"/paper/2007.13544/integrity","json":"/paper/2007.13544/citation-record.json","paper":"/paper/2007.13544"},"outbound":[],"paper":{"arxiv_id":"2007.13544","last_updated":"2020-11-29T03:18:13Z","latest_version":2,"primary_category":"cs.GT","snapshot_observed_at":"2026-08-09T05:50:47.015160Z","submitted_at":"2020-07-27T15:21:22Z","title":"Combining Deep Reinforcement Learning and Search for Imperfect-Information Games"},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2007.13544."}