{"as_of":"2026-08-15T15:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d808138fe54fa0ecd6afe7bcbabfeca218cb78d36b902247eee1b7d92d06afa2","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T08:58:33.334458Z","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":"2026-07-03T08:47:50.454414Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.10410","last_updated":"2020-11-29T12:23:34Z","snapshot_observed_at":"2026-08-07T01:32:42.645310Z","submitted_at":"2020-06-18T10:30:27Z","title":"DREAM: Deep Regret minimization with Advantage baselines and Model-free learning","version":2},"cited_work":{"arxiv_id":"2006.10410","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.10410","snapshot_observed_at":"2026-07-03T08:47:50.454414Z","title":"DREAM: deep regret minimization with advantage baselines and model-free learning.CoRR, abs/2006.10410","venue":null,"work_id":"54f3811a-9605-4231-ad46-c189306356dd","year":2006},"citing_paper":{"arxiv_id":"2510.18183","last_updated":"2026-08-03T05:53:48Z","snapshot_observed_at":"2026-08-15T01:40:50.421727Z","submitted_at":"2025-10-21T00:14:45Z","title":"NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-18T05:48:29.884438Z"},"links":{"cited_paper":"/paper/2006.10410","citing_paper":"/paper/2510.18183"},"observation_digest":"sha256:2f8a752d13a92d8f2031d4d9f09c7c8268fbfa89bc4d07eb93e329183481523b","observation_id":"354b19a5-705b-4b70-b2cc-525675aaf223","resolution":{"observed_at":"2026-05-18T05:50:56.491121Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10410","last_updated":"2020-11-29T12:23:34Z","snapshot_observed_at":"2026-08-07T01:32:42.645310Z","submitted_at":"2020-06-18T10:30:27Z","title":"DREAM: Deep Regret minimization with Advantage baselines and Model-free learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10410","snapshot_observed_at":"2026-08-04T08:58:33.334458Z","title":"DREAM: deep regret minimization with advantage baselines and model-free learning.CoRR, abs/2006.10410, 2020","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2510.18183","last_updated":"2026-08-03T05:53:48Z","snapshot_observed_at":"2026-08-15T01:40:50.421727Z","submitted_at":"2025-10-21T00:14:45Z","title":"NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T08:58:33.334458Z"},"links":{"cited_paper":"/paper/2006.10410","citing_paper":"/paper/2510.18183"},"observation_digest":"sha256:33d3da6417af5d25fbdb75dc08e39b2a750b26b4616d1a6b77c6961191648374","observation_id":"47174499-4607-4911-b25f-e7df15edd7d8","resolution":{"observed_at":"2026-08-04T08:58:33.334458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10410","last_updated":"2020-11-29T12:23:34Z","snapshot_observed_at":"2026-08-07T01:32:42.645310Z","submitted_at":"2020-06-18T10:30:27Z","title":"DREAM: Deep Regret minimization with Advantage baselines and Model-free learning","version":2},"cited_work":{"arxiv_id":"2006.10410","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.10410","snapshot_observed_at":"2026-07-03T08:47:50.454414Z","title":"DREAM: deep regret minimization with advantage baselines and model-free learning.CoRR, abs/2006.10410","venue":null,"work_id":"54f3811a-9605-4231-ad46-c189306356dd","year":2006},"citing_paper":{"arxiv_id":"2605.09150","last_updated":"2026-05-09T20:26:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-09T20:26:35Z","title":"AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-12T04:45:20.479345Z"},"links":{"cited_paper":"/paper/2006.10410","citing_paper":"/paper/2605.09150"},"observation_digest":"sha256:ab5afcf7662016b7933dc03b4a8b0e5bb3fa3e62f3a99c2b73c38aae14091347","observation_id":"9dca485e-dacb-4fca-af6b-983af41b4c9d","resolution":{"observed_at":"2026-05-12T05:56:35.028632Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10410","last_updated":"2020-11-29T12:23:34Z","snapshot_observed_at":"2026-08-07T01:32:42.645310Z","submitted_at":"2020-06-18T10:30:27Z","title":"DREAM: Deep Regret minimization with Advantage baselines and Model-free learning","version":2},"cited_work":{"arxiv_id":"2006.10410","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.10410","snapshot_observed_at":"2026-07-03T08:47:50.454414Z","title":"DREAM: deep regret minimization with advantage baselines and model-free learning.CoRR, abs/2006.10410","venue":null,"work_id":"54f3811a-9605-4231-ad46-c189306356dd","year":2006},"citing_paper":{"arxiv_id":"2605.14379","last_updated":"2026-05-14T05:00:52Z","snapshot_observed_at":"2026-08-03T07:32:37.985918Z","submitted_at":"2026-05-14T05:00:52Z","title":"Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-15T02:40:52.593068Z"},"links":{"cited_paper":"/paper/2006.10410","citing_paper":"/paper/2605.14379"},"observation_digest":"sha256:25aacd5adbe5e8111e0073c7f3137b62ca15fa8a93cf071f015a9b31e7653b4a","observation_id":"b0a9814d-518e-4306-80ad-1c59bca55711","resolution":{"observed_at":"2026-05-15T02:49:41.639523Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10410","last_updated":"2020-11-29T12:23:34Z","snapshot_observed_at":"2026-08-07T01:32:42.645310Z","submitted_at":"2020-06-18T10:30:27Z","title":"DREAM: Deep Regret minimization with Advantage baselines and Model-free learning","version":2},"cited_work":{"arxiv_id":"2006.10410","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.10410","snapshot_observed_at":"2026-07-03T08:47:50.454414Z","title":"DREAM: deep regret minimization with advantage baselines and model-free learning.CoRR, abs/2006.10410","venue":null,"work_id":"54f3811a-9605-4231-ad46-c189306356dd","year":2006},"citing_paper":{"arxiv_id":"2606.11284","last_updated":"2026-06-09T16:40:26Z","snapshot_observed_at":"2026-08-14T13:15:39.247912Z","submitted_at":"2026-06-09T16:40:26Z","title":"Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-06-27T10:42:17.852996Z"},"links":{"cited_paper":"/paper/2006.10410","citing_paper":"/paper/2606.11284"},"observation_digest":"sha256:bcc2b37df3bfbbe9a346692dba2c0de8bec0d050da8685baf802a0fffd7152aa","observation_id":"665cb4b6-3870-4105-9c33-292e9d29707e","resolution":{"observed_at":"2026-07-03T08:47:50.455662Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10410","last_updated":"2020-11-29T12:23:34Z","snapshot_observed_at":"2026-08-07T01:32:42.645310Z","submitted_at":"2020-06-18T10:30:27Z","title":"DREAM: Deep Regret minimization with Advantage baselines and Model-free learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10410","snapshot_observed_at":"2026-07-30T13:10:40.392185Z","title":"Dream: Deep regret minimization with advantage baselines and model-free learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.27035","last_updated":"2026-07-29T15:28:36Z","snapshot_observed_at":"2026-08-08T19:05:43.842976Z","submitted_at":"2026-07-29T15:28:36Z","title":"Correlated Chance Sampling for Monte Carlo Counterfactual Regret Minimization","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-07-30T13:10:40.392185Z"},"links":{"cited_paper":"/paper/2006.10410","citing_paper":"/paper/2607.27035"},"observation_digest":"sha256:9c9b0c5bbd792e19081813bb97699bdcd54c031f625994a62908cc49be6cd5b4","observation_id":"be6e0349-e3b3-4541-ada6-d87c88483f77","resolution":{"observed_at":"2026-07-30T13:10:40.392185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2006.10410/citation-record","integrity":"/paper/2006.10410/integrity","json":"/paper/2006.10410/citation-record.json","paper":"/paper/2006.10410"},"outbound":[],"paper":{"arxiv_id":"2006.10410","last_updated":"2020-11-29T12:23:34Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T01:32:42.645310Z","submitted_at":"2020-06-18T10:30:27Z","title":"DREAM: Deep Regret minimization with Advantage baselines and Model-free 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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2006.10410."}