{"as_of":"2026-08-09T22:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:798c81b1e1ded1c415a0d737cdcb861b723540dddd4d53481e0d35a1ff451c88","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T01:03:57.025841Z","state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T07:56:46.797195Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.03723","snapshot_observed_at":"2026-08-04T07:56:46.797195Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.24803","last_updated":"2026-07-15T00:10:20Z","snapshot_observed_at":"2026-08-08T21:42:31.396671Z","submitted_at":"2025-10-28T00:48:20Z","title":"MASPRM: Multi-Agent System Process Reward Model","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-04T07:56:46.797195Z"},"links":{"cited_paper":"/paper/2502.03723","citing_paper":"/paper/2510.24803"},"observation_digest":"sha256:00c0ac68caeaeb15dccc6d44b46ba4a673fd6bb5ae56cde1174459db7e6045a0","observation_id":"61540494-045a-46b4-b6e9-baa27c71ad69","resolution":{"observed_at":"2026-08-04T07:56:46.797195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.03723","snapshot_observed_at":"2026-07-31T21:55:17.425466Z","title":"arXiv preprint arXiv:2502.03723 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27967","last_updated":"2026-07-30T10:14:24Z","snapshot_observed_at":"2026-08-05T12:14:09.489312Z","submitted_at":"2026-07-30T10:14:24Z","title":"MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-07-31T21:55:17.425466Z"},"links":{"cited_paper":"/paper/2502.03723","citing_paper":"/paper/2607.27967"},"observation_digest":"sha256:3f1f675ccc843ab80cccb33eef4865a3c03f8011dab86542446195ae7a20d485","observation_id":"be47df70-0bf2-4a7c-ad41-141f96d55027","resolution":{"observed_at":"2026-07-31T21:55:17.425466Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.03723/citation-record","integrity":"/paper/2502.03723/integrity","json":"/paper/2502.03723/citation-record.json","paper":"/paper/2502.03723"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-09T01:03:56.956730Z","title":"L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.956730Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:260aa65797edbe4badfe681c0848252ede2421915a8c5bc38f63d28da428fd61","observation_id":"85dd0847-b73e-4d6f-b06c-61836ec1bfe7","resolution":{"observed_at":"2026-08-09T01:03:56.956730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.00267","last_updated":"2024-09-03T14:01:54Z","snapshot_observed_at":"2026-07-06T16:13:07.384791Z","submitted_at":"2023-09-01T05:53:33Z","title":"RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.00267","snapshot_observed_at":"2026-08-09T01:03:56.972646Z","title":"R., Bishop, C., Hall, E., Carbune, V ., Rastogi, A., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.972646Z"},"links":{"cited_paper":"/paper/2309.00267","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:32b85103530c6d687f016a5ce7509c941e94f24ba364ea8c175fb17ba5a341a6","observation_id":"da73d674-217f-498e-b824-a679cfa9a230","resolution":{"observed_at":"2026-08-09T01:03:56.972646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.05091","last_updated":"2021-06-09T14:10:50Z","snapshot_observed_at":"2026-08-09T12:10:10.862824Z","submitted_at":"2021-06-09T14:10:50Z","title":"PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.05091","snapshot_observed_at":"2026-08-09T01:03:56.977920Z","title":"Pebble: Feedback- efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.977920Z"},"links":{"cited_paper":"/paper/2106.05091","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:ff22ae3e8a4efbd4fa12200698bb7da000ca92294a3a4490b3f48748ea6da904","observation_id":"67a4b0f7-931d-4a38-82ba-3573b732027a","resolution":{"observed_at":"2026-08-09T01:03:56.977920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.12306","last_updated":"2020-12-18T15:16:06Z","snapshot_observed_at":"2026-07-06T09:41:25.799628Z","submitted_at":"2020-07-24T00:50:02Z","title":"Value-Decomposition Multi-Agent Actor-Critics","version":4},"cited_work":{"arxiv_id":"2007.12306","doi":null,"metadata_source":"pith","pith_arxiv_id":"2007.12306","snapshot_observed_at":"2026-08-09T01:03:57.162686Z","title":"Value-Decomposition Multi-Agent Actor-Critics","venue":"cs.AI","work_id":"6d9a42f5-5650-4965-9ca0-d63618a3335f","year":2020},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.995039Z"},"links":{"cited_paper":"/paper/2007.12306","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:3a52ccd25ecfcd2bbb995898e273a2513d07059ca7b7ff27a1605fa547ecbcca","observation_id":"f4c0a592-b42c-4738-a2c3-0f7e97a1021d","resolution":{"observed_at":"2026-08-09T01:03:57.167804Z","resolver_source":"local_arxiv","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":"2401.04056","last_updated":"2024-06-13T14:45:36Z","snapshot_observed_at":"2026-08-09T09:34:04.204036Z","submitted_at":"2024-01-08T17:55:02Z","title":"A Minimaximalist Approach to Reinforcement Learning from Human Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04056","snapshot_observed_at":"2026-08-09T01:03:57.000001Z","title":"S., and Agarwal, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:57.000001Z"},"links":{"cited_paper":"/paper/2401.04056","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:b018368bb2616e480a3b2de5fdfc5cc57042d7a001f281ad5b9b7a510f5fde0d","observation_id":"07a7d512-4dd4-49e0-a511-8fea0fc40018","resolution":{"observed_at":"2026-08-09T01:03:57.000001Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.01062","last_updated":"2021-10-04T01:36:59Z","snapshot_observed_at":"2026-07-06T09:44:11.377445Z","submitted_at":"2020-08-03T17:52:09Z","title":"QPLEX: Duplex Dueling Multi-Agent Q-Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.01062","snapshot_observed_at":"2026-08-09T01:03:57.010803Z","title":"Qplex: Duplex dueling multi-agent q-learning","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:57.010803Z"},"links":{"cited_paper":"/paper/2008.01062","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:ea897fb3fcdf9f91a84a19de1d4738e6ee3428a890cd49374ca63fafd1b77721","observation_id":"a4bd5be9-8ec5-452e-9614-099159e616da","resolution":{"observed_at":"2026-08-09T01:03:57.010803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.01955","last_updated":"2022-11-04T06:16:11Z","snapshot_observed_at":"2026-07-06T10:46:11.856932Z","submitted_at":"2021-03-02T18:59:56Z","title":"The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.01955","snapshot_observed_at":"2026-08-09T01:03:57.021190Z","title":"Zhang, A., Parashar, A., and Saha, D","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:57.021190Z"},"links":{"cited_paper":"/paper/2103.01955","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:506a7f2399ab902c2f7ee2d6fd601ac07c4408301128cbfe494c059589816b8d","observation_id":"f1e0ea21-0d5d-40ff-9d48-64a1ab8391f9","resolution":{"observed_at":"2026-08-09T01:03:57.021190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.08593","last_updated":"2020-01-08T23:02:36Z","snapshot_observed_at":"2026-08-08T07:44:49.921146Z","submitted_at":"2019-09-18T17:33:39Z","title":"Fine-Tuning Language Models from Human Preferences","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.08593","snapshot_observed_at":"2026-08-09T01:03:57.025841Z","title":"10 LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning A","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:57.025841Z"},"links":{"cited_paper":"/paper/1909.08593","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:f590faaaf7d89de60eaef2f2643b9e1ab386132aff9f8c4c23c6386509b379b6","observation_id":"1a12b5d7-adb3-437f-803b-e196361904d1","resolution":{"observed_at":"2026-08-09T01:03:57.025841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1610.03295","last_updated":"2016-10-11T12:09:03Z","snapshot_observed_at":"2026-08-03T05:49:14.071341Z","submitted_at":"2016-10-11T12:09:03Z","title":"Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.03295","snapshot_observed_at":"2026-08-09T01:03:56.990709Z","title":"Safe, multi-agent, reinforcement learning for autonomous driv- ing","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.990709Z"},"links":{"cited_paper":"/paper/1610.03295","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:69ba002db4cbeb9b5db87011c4b933eea950ac26ff6ade21c543a439fee0daad","observation_id":"c015bae3-3cdc-4418-9e2b-b7d71171aa35","resolution":{"observed_at":"2026-08-09T01:03:56.990709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T01:03:57.271285Z","title":"M., Stepputtis, S., Campbell, J., and Sycara, K","venue":null,"work_id":"0bbb7f97-bcc9-4a9f-8e7c-10b6e8edf6e7","year":2024},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.982350Z"},"links":{"citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:f3e262a8676e8e5464f35eefc9b62a45ed9159b8c27e83d3a92494c892c214a1","observation_id":"71e1e9c4-e878-4489-a434-6e91cfa7c096","resolution":{"observed_at":"2026-08-09T01:03:57.276158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T01:03:57.255726Z","title":null,"venue":null,"work_id":"2f7da943-f771-4449-8ac7-12f5bb6b1137","year":2003},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.986632Z"},"links":{"citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:dc815afd147d9fc702bd20ee613398268a168ea7d9fabc09cc5a5decdd8e413d","observation_id":"fd87cffd-a3a8-4b0c-9528-235a4f84bdcb","resolution":{"observed_at":"2026-08-09T01:03:57.260440Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-09T01:03:57.005707Z","title":"cc/paper_files/paper/2021/file/ 7ed2d3454c5eea71148b11d0c25104ff-Paper","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:57.005707Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:f5e052e866fbaaa82461009a7955129f97fb4da59ff59ba6778e6c29f35c3569","observation_id":"081a3b8a-f58e-4007-8964-81aa60c348bd","resolution":{"observed_at":"2026-08-09T01:03:57.005707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.14953","last_updated":"2022-10-28T10:41:49Z","snapshot_observed_at":"2026-07-06T13:15:21.126555Z","submitted_at":"2022-05-30T09:39:45Z","title":"Multi-Agent Reinforcement Learning is a Sequence Modeling Problem","version":3},"cited_work":{"arxiv_id":"2205.14953","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.14953","snapshot_observed_at":"2026-08-09T01:03:57.093434Z","title":"Multi-Agent Reinforcement Learning is a Sequence Modeling Problem","venue":"cs.MA","work_id":"760f49df-21c5-4658-b9ef-afb293654ff7","year":2022},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:57.016285Z"},"links":{"cited_paper":"/paper/2205.14953","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:480a9480dd04eef1d376835d45fc6c21b2823095d94560d3fb0825249973b927","observation_id":"b66be455-9538-49cb-a6d0-4c0848d20552","resolution":{"observed_at":"2026-08-09T01:03:57.100578Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T01:03:57.286138Z","title":"A variational approach to mutual information-based coordination for multi-agent reinforcement learning","venue":null,"work_id":"db6c8b6e-c9a3-4a07-b5e2-779db4b8c1cd","year":2023},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.967507Z"},"links":{"citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:34770f3d8e42ce690783aa58e76fb6730526acc3212661b46bd07e128dd7a466","observation_id":"8ea196f9-74d8-4fd3-b484-b654409ff70e","resolution":{"observed_at":"2026-08-09T01:03:57.290368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2408.04295","last_updated":"2025-02-07T10:48:22Z","snapshot_observed_at":"2026-08-02T02:46:54.388016Z","submitted_at":"2024-08-08T08:18:05Z","title":"Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.04295","snapshot_observed_at":"2026-08-09T01:03:56.962583Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-09T01:03:56.962583Z"},"links":{"cited_paper":"/paper/2408.04295","citing_paper":"/paper/2502.03723"},"observation_digest":"sha256:5e828c407f759197ff815f393517ba19913dc70c50d9dd48815198f14c64c1ab","observation_id":"cff30953-901f-495c-928c-8f3660cacebc","resolution":{"observed_at":"2026-08-09T01:03:56.962583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.03723","last_updated":"2025-03-01T01:43:31Z","latest_version":2,"primary_category":"cs.MA","snapshot_observed_at":"2026-08-09T00:55:51.557969Z","submitted_at":"2025-02-06T02:26:47Z","title":"Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":2,"verified_fuzzy":2},"total_outbound_references":15},"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 15 of 15 outbound references and 2 inbound Pith citation observations for arXiv:2502.03723."}