{"as_of":"2026-08-20T05:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6b7ada0c75073a500c2dfaca4814565f7a01f175d427bd46b63ae6e8aba35419","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-09T09:08:14.104220Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2607.07498/citation-record","integrity":"/paper/2607.07498/integrity","json":"/paper/2607.07498/citation-record.json","paper":"/paper/2607.07498"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T09:16:06.915527Z","title":"Automated video game testing using synthetic and humanlike agents,","venue":null,"work_id":"32e3ae9c-8021-4449-9283-110fdffe6960","year":2019},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:ff75f83a0db9a85b659b0b6ec46ac2a054f02eca4a172fc66662dc95ac4d4013","observation_id":"5c4be205-f414-44d0-a084-bba86c3f92e4","resolution":{"observed_at":"2026-07-09T09:16:06.916723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.909826Z","title":"Automated play-testing through rl based human-like play-styles generation,","venue":null,"work_id":"f9b38f94-124d-4db5-a2ab-c2191b039409","year":2022},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:b0fadc6eedbe79eb8f4e8202429fd4d569d8fa1d724e28311df360057f5b2ea5","observation_id":"de5ef3f2-1a9a-4a03-90e3-4cd8786354ae","resolution":{"observed_at":"2026-07-09T09:16:06.911198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.911776Z","title":"Augmenting automated game testing with deep rein- forcement learning,","venue":null,"work_id":"7e6918e9-b9dc-46f9-bbf2-984bede0ef12","year":2020},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:45ff5fa773794148b11250e5d2fa4a8b924f4dcafe5b75bc2f3b8bd697fbd6ab","observation_id":"5c186563-60a5-4ace-898b-c571a551f425","resolution":{"observed_at":"2026-07-09T09:16:06.913122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.907754Z","title":"Emergent tool use from multi-agent autocurricula,","venue":null,"work_id":"d6f815d4-f492-4396-9c22-6fb309f4b2d3","year":2019},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:863e08e4e9e9c4b3a953df8e04090fcc5e2733a50b609da4afc39906a4d86d28","observation_id":"899641aa-4431-48e2-b98e-f5e03b74cbb8","resolution":{"observed_at":"2026-07-09T09:16:06.909058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.919418Z","title":"Improving playtesting coverage via curiosity driven reinforcement learning agents,","venue":null,"work_id":"5a094c98-35d2-424b-9557-6852e428b518","year":2021},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:8d2de8c8c34bc92cb163605fe294215c391483d3d7c6f3fd3208e571111db503","observation_id":"846541da-ebc8-4165-9103-81946fe86a0e","resolution":{"observed_at":"2026-07-09T09:16:06.920709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.921231Z","title":"Automated gameplay testing and validation with curiosity-conditioned proximal trajectories,","venue":null,"work_id":"d6522e11-1386-40dc-a43f-085027c4c35c","year":2022},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:47256f9ef101c631325d5ca6ed3ee230e7562b1619f4df02abf3af86d05501ec","observation_id":"cca9e18e-a23e-4628-9b85-e002889c6b94","resolution":{"observed_at":"2026-07-09T09:16:06.922406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.912084Z","title":"Discovering policies with DOMiNO: Diversity optimization maintaining near optimality,","venue":null,"work_id":"7dd4125a-dec9-41bb-8c0e-e0ca11580a24","year":null},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:deaeb2e39b290ac05d77d13ff68a2d7f73022a1107015a175d9859b2b2ec2e32","observation_id":"106edc69-2e7e-42c7-8053-cdefe9aa59ca","resolution":{"observed_at":"2026-07-09T09:16:06.913452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.915842Z","title":"Available: https://openreview.net/forum? id=kjkdzBW3b8p","venue":null,"work_id":"80d32208-c703-4210-99e8-b4608f8a1b13","year":null},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:44ff47cdd59dc40420095f66b7a5186a11893e733af0dd4adc111cf6415febfb","observation_id":"73ecc8ab-29b6-471d-9f55-205785a95876","resolution":{"observed_at":"2026-07-09T09:16:06.917237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.906270Z","title":"Discovering creative behaviors through du- plex: Diverse universal features for policy exploration,","venue":null,"work_id":"15237cf1-2f88-45a3-91c4-adddbb05980b","year":2024},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:00af5cd009477e4e6011eebd03d5dca31524618a1885f2bcc9c6fdd2efd4f481","observation_id":"2296dcd6-1530-4913-87d3-9cd931d3db44","resolution":{"observed_at":"2026-07-09T09:16:06.907424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.913665Z","title":"Grandmaster level in starcraft ii using multi-agent reinforcement learning,","venue":null,"work_id":"0226c6b5-aea2-4c15-9e44-83c69837d412","year":2019},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:8a6551c269b22c16184a6f14527115b86bb59029aee090858a88f357cd55a41b","observation_id":"c2e5b2ba-a4c8-47e5-9e73-351249d213b4","resolution":{"observed_at":"2026-07-09T09:16:06.914972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.917418Z","title":"Outracing champion gran tur- ismo drivers with deep reinforcement learning,","venue":null,"work_id":"952e4703-0314-4273-afaa-85554b715934","year":2022},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:e06ce579f531f037fe68fbbccfaf3b564c114ce3eb5712bd0886c6c6f304fca2","observation_id":"3409aca1-8368-43d5-ad77-e6396f58524a","resolution":{"observed_at":"2026-07-09T09:16:06.918847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.899285Z","title":"Automated playtesting with procedural personas through mcts with evolved heuristics,","venue":null,"work_id":"5c5c42af-8471-47aa-b9dd-ed76a4d4db7a","year":2019},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:430dd89f3a31fae1ab2169cab9489d880cc9e3f7eccce1a22a5dded368b9fbc2","observation_id":"8e80f8dc-1702-4875-83d0-7fd826c8dd28","resolution":{"observed_at":"2026-07-09T09:16:06.900463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.900979Z","title":"Navigation turing test (ntt): Learning to evaluate human-like navigation,","venue":null,"work_id":"027c2c1c-6efa-42d3-b42c-f75dd28010d1","year":2021},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:34dbb883687c029aa5955e75a3682186388e99921d26d96aaf82cdb63a4eda77","observation_id":"87a1a230-4c72-40a4-9d44-c8e3974a0e1f","resolution":{"observed_at":"2026-07-09T09:16:06.902092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1576.200160","doi":"10.1145/2001576.2001606","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Stanley , editor =","venue":null,"work_id":"33dcd11c-5762-4232-8af5-7f48166bab73","year":2011},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:65917a3685d1a947063628a9fe5e02461a380308033fc68de1f0aea507fbe6b8","observation_id":"740098e4-6fd7-4279-8e24-540150e5a837","resolution":{"observed_at":"2026-07-09T09:16:06.673683Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-07-09T10:48:26.810209+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-09T10:48:26.810209+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1504.04909","last_updated":"2015-04-20T01:17:00Z","snapshot_observed_at":"2026-08-15T13:07:45.856087Z","submitted_at":"2015-04-20T01:17:00Z","title":"Illuminating search spaces by mapping elites","version":1},"cited_work":{"arxiv_id":"1504.04909","doi":"10.48550/arxiv.1504.04909","metadata_source":"pith","pith_arxiv_id":"1504.04909","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Illuminating search spaces by mapping elites","venue":"cs.AI","work_id":"f533394d-ecf0-413c-b423-c92c67d478d9","year":2015},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"cited_paper":"/paper/1504.04909","citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:711a1cc40b13b5299b03b7040f4c0a210093d66707f5f085af38ca5f225e6c5c","observation_id":"0e8db9ca-956e-450e-b36e-9b6fac1f0895","resolution":{"observed_at":"2026-07-09T09:16:06.700898Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-05-23T21:52:57.312881+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T21:52:57.312881+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.905943Z","title":"Robots that can adapt like animals","venue":null,"work_id":"245a35a0-e527-4636-812b-59802be5c6cc","year":2015},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:97f500f4aa142962eb4d78d55ede5689f2eb9abcb30d14d8af5e683faddcde15","observation_id":"7ab1e778-5804-4c9c-9d8e-84ecad2637a6","resolution":{"observed_at":"2026-07-09T09:16:06.907215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.892610Z","title":"Variational intrinsic control,","venue":null,"work_id":"0ecbdeaa-09b7-4ea5-8e7e-a8241c78f8a3","year":2017},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:930db1e8c0e9b683e31e355046c1316969a17e1170970e1aa80214d842f61bb3","observation_id":"2617b097-d43d-4c3c-8db2-e9e1f77fafa1","resolution":{"observed_at":"2026-07-09T09:16:06.894031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.890788Z","title":"Diversity is all you need: Learning skills without a reward function,","venue":null,"work_id":"54c0d5c9-ab71-415d-a214-02206f396155","year":2019},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:f534c6c1f9e38122a657828e52639ad7e0f822b7dc4f2d7653bc11a00f2f0331","observation_id":"9e8e45b5-0b43-4c16-b247-3d015b492d23","resolution":{"observed_at":"2026-07-09T09:16:06.892108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.897603Z","title":"Successor features for transfer in reinforcement learning,","venue":null,"work_id":"f48f8393-feed-4638-8ee9-1421ce84c9d2","year":2017},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:7332055779b31a8e870915819d77191741b9dab3497a499a93e17b1a15c5f229","observation_id":"830aba0e-e976-4acd-a93a-b48abe0a1983","resolution":{"observed_at":"2026-07-09T09:16:06.898747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.887053Z","title":"Policy invariance under reward transformations: Theory and application to reward shaping","venue":null,"work_id":"a16e57da-87bb-476f-bead-1bdebcf23d23","year":1999},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:39c052c8c554bfd359ad58f6c71997ec291b46a02a5a1a790670a9b975aac2cb","observation_id":"910ac1d6-362b-4cc9-bbaf-dd5568be7256","resolution":{"observed_at":"2026-07-09T09:16:06.888320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.892285Z","title":null,"venue":null,"work_id":"c9a9933e-0e5f-4622-a46b-032fde3449f5","year":2018},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:1a4e921517cdcb04cb4773ed1c0fd1f0078fcda190301faa7f71221f10f0463c","observation_id":"91f57102-47d7-4519-b353-28e5bba13ded","resolution":{"observed_at":"2026-07-09T09:16:06.893886Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.888856Z","title":"Soft actor-critic: Off-policy maximum entropy deep reinforce- ment learning with a stochastic actor,","venue":null,"work_id":"60894c83-b00f-4240-b922-d53119eb04b5","year":2018},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:b60ee2d68dd9653c058a044e55cbddadbaa1f04c73834af1dcf7f194c9ad1648","observation_id":"1b2be37c-750a-419c-bd57-43d72fea8cab","resolution":{"observed_at":"2026-07-09T09:16:06.890293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.11077","last_updated":"2019-12-23T19:37:13Z","snapshot_observed_at":"2026-08-09T06:19:09.736484Z","submitted_at":"2019-12-23T19:37:13Z","title":"Discrete and Continuous Action Representation for Practical RL in Video Games","version":1},"cited_work":{"arxiv_id":"1912.11077","doi":null,"metadata_source":"pith","pith_arxiv_id":"1912.11077","snapshot_observed_at":"2026-07-09T09:16:06.702133Z","title":"Discrete and Continuous Action Representation for Practical RL in Video Games","venue":"cs.LG","work_id":"5f3dc8cc-0055-4bdc-b6ce-c56d80e879ae","year":2019},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"cited_paper":"/paper/1912.11077","citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:572ed8f9b99e4ec63de9b0ac38e253d39b5e4b08156e404416a4a6e07d4ab06d","observation_id":"ca0e207e-3619-4b4c-94ac-29909f7bfe83","resolution":{"observed_at":"2026-07-09T09:16:06.703424Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-07-09T09:16:06.904529Z","title":"Some methods of classification and analysis of multivariate observations,","venue":null,"work_id":"d10a0911-b31f-4267-a014-0ab6730d6e31","year":1967},"citing_paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-09T09:08:14.104220Z"},"links":{"citing_paper":"/paper/2607.07498"},"observation_digest":"sha256:ee58eda46b25ae20466337d43d4edaedfebbb1861bb4090b35bf6805b6f2eaf5","observation_id":"39ca2de1-9a5f-4d76-ba9c-9056b6cf8afb","resolution":{"observed_at":"2026-07-09T09:16:06.905768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.07498","last_updated":"2026-07-08T14:57:39Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T22:13:42.029203Z","submitted_at":"2026-07-08T14:57:39Z","title":"Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":1,"verified_exact":2,"verified_fuzzy":20},"total_outbound_references":24},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.07498."}