{"as_of":"2026-08-14T17:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bc182872a96b9aa8d4186bd80f1f7db5b757e2a5d3b01db3ec8a0e4a6451dac9","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T04:15:06.330124Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2607.29577/citation-record","integrity":"/paper/2607.29577/integrity","json":"/paper/2607.29577/citation-record.json","paper":"/paper/2607.29577"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.09453","last_updated":"2020-09-26T11:49:05Z","snapshot_observed_at":"2026-08-14T11:08:51.634209Z","submitted_at":"2019-08-26T03:31:35Z","title":"OpenSpiel: A Framework for Reinforcement Learning in Games","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.09453","snapshot_observed_at":"2026-08-03T04:15:05.822497Z","title":"Openspiel: A framework for reinforcement learning in games.arXiv preprint arXiv:1908.09453,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:05.822497Z"},"links":{"cited_paper":"/paper/1908.09453","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:4d2f7ad44cc8297e768fb1391161d09811e2f001faadfc669891e332c94f01d1","observation_id":"db8355d4-070f-43c9-bf89-5ad59f728689","resolution":{"observed_at":"2026-08-03T04:15:05.822497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16801","last_updated":"2024-06-03T14:12:27Z","snapshot_observed_at":"2026-08-13T04:09:49.312332Z","submitted_at":"2024-02-26T18:19:07Z","title":"Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16801","snapshot_observed_at":"2026-08-03T04:15:05.886907Z","title":"Craftax: A lightning-fast benchmark for open-ended reinforcement learning.arXiv preprint arXiv:2402.16801,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:05.886907Z"},"links":{"cited_paper":"/paper/2402.16801","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:7c3c4686d1554d950c42ae6903e940fdfe721de9438bb84d9df580c9902e5eba","observation_id":"445e54d1-df97-4dd7-9041-7ee7213ad898","resolution":{"observed_at":"2026-08-03T04:15:05.886907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.13543","last_updated":"2025-04-01T14:45:22Z","snapshot_observed_at":"2026-08-13T22:17:36.984752Z","submitted_at":"2024-11-20T18:54:32Z","title":"BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.13543","snapshot_observed_at":"2026-08-03T04:15:06.000334Z","title":"Carlo Romeo and Andrew D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:06.000334Z"},"links":{"cited_paper":"/paper/2411.13543","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:437e6ceed3fb6715fca0327795f589eafc03aad8ef0ee8cf311ab429360429af","observation_id":"c3cfa10d-5cd9-48a2-8036-69dc7653fd14","resolution":{"observed_at":"2026-08-03T04:15:06.000334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.04043","last_updated":"2019-12-09T07:26:52Z","snapshot_observed_at":"2026-08-10T17:28:09.498763Z","submitted_at":"2019-02-11T18:43:53Z","title":"The StarCraft Multi-Agent Challenge","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.04043","snapshot_observed_at":"2026-08-03T04:15:06.067394Z","title":"The starcraft multi-agent challenge.arXiv preprint arXiv:1902.04043,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:06.067394Z"},"links":{"cited_paper":"/paper/1902.04043","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:7f181ccb4bcfd6af523c412a7c4a3fbbc0cbcc9f63c1a89b44d0811166f764af","observation_id":"e7bac09c-82ec-40ea-aff7-408267d2403b","resolution":{"observed_at":"2026-08-03T04:15:06.067394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01557","last_updated":"2024-03-17T23:23:31Z","snapshot_observed_at":"2026-08-13T05:58:59.275924Z","submitted_at":"2023-10-02T18:52:11Z","title":"SmartPlay: A Benchmark for LLMs as Intelligent Agents","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01557","snapshot_observed_at":"2026-08-03T04:15:06.235494Z","title":"Zelai Xu, Ruize Zhang, Chao Yu, Huining Yuan, Xiangmin Yi, Shilong Ji, Wenhao Tang, Feng Gao, Wenbo Ding, Xinlei Chen, and Yu Wang","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:06.235494Z"},"links":{"cited_paper":"/paper/2310.01557","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:b3c7eb2a82845c2db4dbb0b821d32bc67bdb1ef0af01ff85ada8c7e887d0f213","observation_id":"aa1993d8-c6eb-4706-9ca9-77f389addc25","resolution":{"observed_at":"2026-08-03T04:15:06.235494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03629","last_updated":"2023-03-10T01:00:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-10-06T01:00:32Z","title":"ReAct: Synergizing Reasoning and Acting in Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03629","snapshot_observed_at":"2026-08-03T04:15:06.330124Z","title":"choice_id","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:06.330124Z"},"links":{"cited_paper":"/paper/2210.03629","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:cf783e0469b1ad7948df75301f3d92749350a70d1edc7c0b33151c1927d43f01","observation_id":"9d3a51d0-1f30-4ad5-a83f-f6ab905d3787","resolution":{"observed_at":"2026-08-03T04:15:06.330124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.06780","last_updated":"2022-02-12T20:02:13Z","snapshot_observed_at":"2026-08-13T18:12:01.546006Z","submitted_at":"2021-09-14T15:49:31Z","title":"Benchmarking the Spectrum of Agent Capabilities","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.06780","snapshot_observed_at":"2026-08-03T04:15:05.687146Z","title":"Benchmarking the spectrum of agent capabilities.arXiv preprint arXiv:2109.06780,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:05.687146Z"},"links":{"cited_paper":"/paper/2109.06780","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:758514ad7d814c54ca60af76834b043e0010a863201021b8cf771bd256522d3d","observation_id":"c762bc2e-bd9d-408f-a772-383437155fa3","resolution":{"observed_at":"2026-08-03T04:15:05.687146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.15726","last_updated":"2025-03-19T22:48:20Z","snapshot_observed_at":"2026-08-07T16:50:31.404615Z","submitted_at":"2025-03-19T22:48:20Z","title":"Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.15726","snapshot_observed_at":"2026-08-03T04:15:05.479917Z","title":"Ogbinar, and Prospero C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:05.479917Z"},"links":{"cited_paper":"/paper/2503.15726","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:cb1ef5cc9b40ede7a371fd5bdd1fa094b1edcbaef7e788973dae3ee92f6ce790","observation_id":"7ac0af2d-135f-4ac6-bca6-709b74873b7b","resolution":{"observed_at":"2026-08-03T04:15:05.479917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.09617","last_updated":"2025-03-06T20:13:02Z","snapshot_observed_at":"2026-08-12T18:48:17.834630Z","submitted_at":"2025-03-06T20:13:02Z","title":"Factorio Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.09617","snapshot_observed_at":"2026-08-03T04:15:05.755199Z","title":"Factorio learning environment.arXiv preprint arXiv:2503.09617,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:05.755199Z"},"links":{"cited_paper":"/paper/2503.09617","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:7b079af2444a490cfbef587f5cd72318e23fa067259ce02efdc6842f105d2a9e","observation_id":"c7b880de-8f24-4a9e-880a-da7327bf6255","resolution":{"observed_at":"2026-08-03T04:15:05.755199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16291","last_updated":"2023-10-19T16:27:03Z","snapshot_observed_at":"2026-08-13T16:55:46.498156Z","submitted_at":"2023-05-25T17:46:38Z","title":"Voyager: An Open-Ended Embodied Agent with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16291","snapshot_observed_at":"2026-08-03T04:15:06.169450Z","title":"Yue Wu, Xuan Tang, Tom M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:06.169450Z"},"links":{"cited_paper":"/paper/2305.16291","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:7d2652c97ca47aecd120d495ba689f11bdc0a17260a4717cfc03fefb5948756e","observation_id":"dcb1dcb2-117f-4beb-b8f9-8949176a7b53","resolution":{"observed_at":"2026-08-03T04:15:06.169450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01540","last_updated":"2016-06-05T17:54:48Z","snapshot_observed_at":"2026-08-13T12:26:05.192883Z","submitted_at":"2016-06-05T17:54:48Z","title":"OpenAI Gym","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01540","snapshot_observed_at":"2026-08-03T04:15:05.382746Z","title":"Openai gym.arXiv preprint arXiv:1606.01540,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:05.382746Z"},"links":{"cited_paper":"/paper/1606.01540","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:38e5e5ff3eacd06cc70f80b2df27f5c010896ea7d1acc70243f82b1a8e2a5e98","observation_id":"5ae13bb3-adea-4397-ba12-77213ceebecb","resolution":{"observed_at":"2026-08-03T04:15:05.382746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.07489","last_updated":"2023-10-17T14:05:58Z","snapshot_observed_at":"2026-08-13T13:21:50.564389Z","submitted_at":"2022-12-14T20:15:19Z","title":"SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.07489","snapshot_observed_at":"2026-08-03T04:15:05.572715Z","title":"Smacv2: An improved benchmark for cooperative multi-agent reinforce- ment learning.arXiv preprint arXiv:2212.07489,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T04:15:05.572715Z"},"links":{"cited_paper":"/paper/2212.07489","citing_paper":"/paper/2607.29577"},"observation_digest":"sha256:d1c432d13b301525b0c282536cfce14896caf36a776616cce6ec754397afbee9","observation_id":"4bcbad1a-ce27-4c67-9e50-ac4cf8d5ab1f","resolution":{"observed_at":"2026-08-03T04:15:05.572715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.29577","last_updated":"2026-07-31T16:03:38Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T09:03:29.633669Z","submitted_at":"2026-07-31T16:03:38Z","title":"DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":12},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2607.29577."}