{"as_of":"2026-08-18T17:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a348c6d4f4e5cd801389b10a630d113a2599f1d84d31b78ecf16dc1b4b6f27c","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":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":33,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:15:55.657239Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":14,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-12T05:26:14.118202Z","title":"Papoudakis , author F","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00431","last_updated":"2024-12-03T15:33:42Z","snapshot_observed_at":"2026-08-18T17:08:15.683161Z","submitted_at":"2024-11-30T10:58:56Z","title":"Multi-Agent System for Cosmological Parameter Analysis","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-12T05:26:14.118202Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2412.00431"},"observation_digest":"sha256:7218ff77cdb879e76ce7072591f895fabaea0a3696b02014408652e932eee94f","observation_id":"8258eed5-3e33-48d4-8b28-38906598eaf5","resolution":{"observed_at":"2026-08-12T05:26:14.118202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-11T11:23:07.074504Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15573","last_updated":"2024-12-20T05:10:34Z","snapshot_observed_at":"2026-08-18T02:58:22.505349Z","submitted_at":"2024-12-20T05:10:34Z","title":"Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T11:23:07.074504Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2412.15573"},"observation_digest":"sha256:33156e7541ed1046a744954333dec20181798342db2af792d5f445f418ca9adc","observation_id":"9dea846f-af24-400c-af1a-cd58e698a867","resolution":{"observed_at":"2026-08-11T11:23:07.074504Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-10T23:04:37.538087Z","title":"Benchmark- ing multi-agent deep reinforcement learning algorithms in cooperative tasks","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.00160","last_updated":"2024-12-30T22:12:09Z","snapshot_observed_at":"2026-08-10T22:55:40.830079Z","submitted_at":"2024-12-30T22:12:09Z","title":"Deterministic Model of Incremental Multi-Agent Boltzmann Q-Learning: Transient Cooperation, Metastability, and Oscillations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T23:04:37.538087Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2501.00160"},"observation_digest":"sha256:1dd45687f1738fea440936198dd4e0e151917dd945b0ec4326ddfba315fc3891","observation_id":"431129d0-3e96-4b6d-aeea-44cd422c9dfd","resolution":{"observed_at":"2026-08-10T23:04:37.538087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-10T22:38:36.585234Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.01140","last_updated":"2025-01-02T08:47:12Z","snapshot_observed_at":"2026-08-17T01:57:21.312857Z","submitted_at":"2025-01-02T08:47:12Z","title":"Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T22:38:36.585234Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2501.01140"},"observation_digest":"sha256:c6ece69417384e8ef9a6a408bceae047ba15d054a487395c2d0c02744dccbbfd","observation_id":"b59c4fb2-8a3e-4b2f-b114-89373830a529","resolution":{"observed_at":"2026-08-10T22:38:36.585234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-10T21:44:24.124161Z","title":"URL http://link.springer.com/10.1007/ s10458-005-2631-2","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.04180","last_updated":"2025-01-21T14:25:45Z","snapshot_observed_at":"2026-08-18T13:38:23.350378Z","submitted_at":"2025-01-07T23:16:31Z","title":"HIVEX: A High-Impact Environment Suite for Multi-Agent Research (extended version)","version":2},"reference_index":7454,"source":"pdf_text","source_observed_at":"2026-08-10T21:44:24.124161Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2501.04180"},"observation_digest":"sha256:84b471acc43ff943b973f9f1f2d39aa89c4601c12be3f7bc63e3c761b8a8db1f","observation_id":"9259c1f1-2565-413f-be42-00293aa834dd","resolution":{"observed_at":"2026-08-10T21:44:24.124161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-10T19:06:26.997151Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.10593","last_updated":"2025-01-17T22:55:33Z","snapshot_observed_at":"2026-08-18T13:37:45.994748Z","submitted_at":"2025-01-17T22:55:33Z","title":"ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and Assistance","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T19:06:26.997151Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2501.10593"},"observation_digest":"sha256:76afde0d87870441457b5bc6c9bf79e329ab98c3a7326b06d532ce8d446f2240","observation_id":"58a30f00-1af9-4bc3-bc2e-861941e7d3c0","resolution":{"observed_at":"2026-08-10T19:06:26.997151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2502.03506","last_updated":"2026-05-04T09:30:33Z","snapshot_observed_at":"2026-08-12T23:44:58.707794Z","submitted_at":"2025-02-05T12:06:54Z","title":"Optimistic {\\epsilon}-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-23T04:16:54.807723Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2502.03506"},"observation_digest":"sha256:f972a33f314bc89f770dabc4ed1e178b6ca19a1121215f1f426cabdf8c7f813f","observation_id":"d1922028-8854-4da9-97d6-ebce5426a694","resolution":{"observed_at":"2026-05-23T04:17:30.907248Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-09T00:36:02.827053Z","title":"Bench- marking multi-agent deep reinforcement learning algorithms in cooper- ative tasks,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.03845","last_updated":"2025-02-06T07:55:24Z","snapshot_observed_at":"2026-08-17T17:00:37.327979Z","submitted_at":"2025-02-06T07:55:24Z","title":"PAGNet: Pluggable Adaptive Generative Networks for Information Completion in Multi-Agent Communication","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T00:36:02.827053Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2502.03845"},"observation_digest":"sha256:f44f8487653f90f325db40162440c5e43be5fac1b42959f91b532903754a454b","observation_id":"df9c1fca-f757-4566-853e-4a3e2ba998f6","resolution":{"observed_at":"2026-08-09T00:36:02.827053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-15T23:15:55.657239Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.05262","last_updated":"2025-06-12T20:33:40Z","snapshot_observed_at":"2026-08-18T13:35:59.839022Z","submitted_at":"2025-05-08T14:07:20Z","title":"Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T23:15:55.657239Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2505.05262"},"observation_digest":"sha256:58879199482a735d7c5b7d2d86157198fcf905dcf436e25e5264dd8fb2cdb0da","observation_id":"e8d52044-7d32-4448-9c39-ea837d71cd1d","resolution":{"observed_at":"2026-08-15T23:15:55.657239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-07T14:45:41.938999Z","title":"InProceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS)","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.17801","last_updated":"2025-10-28T20:33:31Z","snapshot_observed_at":"2026-08-14T10:23:52.349980Z","submitted_at":"2025-05-23T12:19:18Z","title":"Integrating Counterfactual Simulations with Language Models for Explaining Multi-Agent Behaviour","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T14:45:41.938999Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2505.17801"},"observation_digest":"sha256:1fadcbaccfd203ec47b3b790652a21528a45bbf8625fe2ad7c86ce64f06df991","observation_id":"df623b0f-5afa-4ac8-a8b9-ff2baacf55ce","resolution":{"observed_at":"2026-08-07T14:45:41.938999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-06T23:20:32.068608Z","title":"Papoudakis, F","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18537","last_updated":"2025-06-23T11:47:17Z","snapshot_observed_at":"2026-08-18T15:15:31.678554Z","submitted_at":"2025-06-23T11:47:17Z","title":"Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.068608Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2506.18537"},"observation_digest":"sha256:ba6a552cfc45f6300a7ad80150e3f71cadecb2e69164593be986af71f79e6eef","observation_id":"75e6f2ae-b847-4c12-9093-6f0199de36f7","resolution":{"observed_at":"2026-08-06T23:20:32.068608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-06T19:17:10.904228Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.06004","last_updated":"2025-07-08T14:07:53Z","snapshot_observed_at":"2026-08-18T01:37:54.788759Z","submitted_at":"2025-07-08T14:07:53Z","title":"From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:17:10.904228Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2507.06004"},"observation_digest":"sha256:61fb92ebab4a58adc2246150c69fa36d27ef5ba8469a19acac51de25914e49ec","observation_id":"6f2c22e2-4dfc-4208-bb3d-12a026d54791","resolution":{"observed_at":"2026-08-06T19:17:10.904228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-15T18:20:45.269096Z","title":"Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.18333","last_updated":"2025-07-24T11:59:42Z","snapshot_observed_at":"2026-08-15T18:12:11.573930Z","submitted_at":"2025-07-24T11:59:42Z","title":"Remembering the Markov Property in Cooperative MARL","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T18:20:45.269096Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2507.18333"},"observation_digest":"sha256:33992293eb202051ddd1856b645b37567a23661c7571c0efc76ac6fcdf1e0d9b","observation_id":"4dab9ba7-2994-4682-9406-65442990f983","resolution":{"observed_at":"2026-08-15T18:20:45.269096Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2508.01049","last_updated":"2026-05-13T06:44:44Z","snapshot_observed_at":"2026-08-15T22:13:28.762853Z","submitted_at":"2025-08-01T20:07:25Z","title":"Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-19T01:05:17.086399Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2508.01049"},"observation_digest":"sha256:832b51b23f1afd0c592299b55e5f37c3d9c44ffc7a666ee874533fa0899dc485","observation_id":"9a029f53-bc49-464a-8c19-859f210dd09d","resolution":{"observed_at":"2026-05-19T01:06:57.465247Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T14:49:53.286553Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.20818","last_updated":"2025-08-28T14:16:17Z","snapshot_observed_at":"2026-08-18T13:35:57.288014Z","submitted_at":"2025-08-28T14:16:17Z","title":"cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-05T14:49:53.286553Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2508.20818"},"observation_digest":"sha256:229a8807ddc85543d9eccf287ba0d9483de2c7d367b7128ff9c85e1009f5ba6c","observation_id":"d76f10dd-64d4-4753-840b-ba8f3717fa7f","resolution":{"observed_at":"2026-08-05T14:49:53.286553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2511.14135","last_updated":"2026-04-22T21:56:51Z","snapshot_observed_at":"2026-08-16T08:04:08.851541Z","submitted_at":"2025-11-18T04:48:50Z","title":"AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-17T20:18:09.847453Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2511.14135"},"observation_digest":"sha256:f9d901ed342e8f0b3482a88697ed1b4c3c53f5aeb50a1b694f116d67da2b6610","observation_id":"e196b80d-e2f4-4a5a-a4ad-8a288b05d8f0","resolution":{"observed_at":"2026-05-17T20:20:11.904137Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2511.20857","last_updated":"2026-05-18T16:18:02Z","snapshot_observed_at":"2026-08-11T09:44:01.811561Z","submitted_at":"2025-11-25T21:08:07Z","title":"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-05-14T23:13:15.016486Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2511.20857"},"observation_digest":"sha256:c40f701d4cae921a9698206dd1e00fce3d7f772a8a8a1d2b6153c4db89b4a6cf","observation_id":"79035392-af4c-4926-ab07-8d772f861638","resolution":{"observed_at":"2026-05-14T23:13:15.663980Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-02T22:34:17.452139Z","title":"Bench- marking multi-agent deep reinforcement learning algorithms in cooper- ative tasks,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2603.06607","last_updated":"2026-07-06T14:06:03Z","snapshot_observed_at":"2026-08-10T02:54:17.866304Z","submitted_at":"2026-02-18T14:46:56Z","title":"Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T22:34:17.452139Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2603.06607"},"observation_digest":"sha256:e151520c259c627c33aea729a53ab5e72087ed39e25061e3b94e8c2d94683dc4","observation_id":"df126020-9847-4c4d-94d5-6dc1312ef6d7","resolution":{"observed_at":"2026-08-02T22:34:17.452139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2603.23964","last_updated":"2026-04-13T08:15:41Z","snapshot_observed_at":"2026-08-18T08:35:15.861839Z","submitted_at":"2026-03-25T05:56:54Z","title":"From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-05-15T01:20:03.181903Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2603.23964"},"observation_digest":"sha256:673ceea215ba67d47a528bc38f1fb66b10e62f723021a17ddd6a72d176ef8e3e","observation_id":"c61a811d-1cb9-4766-8d51-a4b0b2a2f09d","resolution":{"observed_at":"2026-05-15T01:23:27.178151Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2604.18190","last_updated":"2026-04-20T12:45:59Z","snapshot_observed_at":"2026-08-13T23:51:30.493278Z","submitted_at":"2026-04-20T12:45:59Z","title":"Scalable Neighborhood-Based Multi-Agent Actor-Critic","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T05:19:42.391924Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2604.18190"},"observation_digest":"sha256:be27b4b90bc4ebb6a8df525ee394d7fbf6146c8881c5ead1cc1287268f6a71ea","observation_id":"61dcb148-354e-44ae-95e4-2426dad23a5f","resolution":{"observed_at":"2026-05-10T09:23:38.040503Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.03842","last_updated":"2026-05-05T15:09:32Z","snapshot_observed_at":"2026-08-12T13:36:17.033459Z","submitted_at":"2026-05-05T15:09:32Z","title":"SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-07T16:29:02.730503Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.03842"},"observation_digest":"sha256:745fa1a86ac08a9641dd307800ec4adfbb535b47a19ca9d417d6824fec1c70fc","observation_id":"f5a7f3e5-60c2-4d85-98c8-10252cef0166","resolution":{"observed_at":"2026-05-11T23:41:17.844145Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.08391","last_updated":"2026-05-19T01:58:16Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T19:00:34Z","title":"SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-12T01:31:19.576223Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.08391"},"observation_digest":"sha256:6438b6936e2f59f2b39d0beca18840e9fa082588e3b246613ee0ee3853bf16f7","observation_id":"157ec0c9-259b-4e87-beed-e437935ab5cb","resolution":{"observed_at":"2026-05-12T07:56:27.325451Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.08391","last_updated":"2026-05-19T01:58:16Z","snapshot_observed_at":"2026-07-06T23:20:38.385189Z","submitted_at":"2026-05-08T19:00:34Z","title":"SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-20T22:34:46.440511Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.08391"},"observation_digest":"sha256:bb96edb0160c019834d922b0ce015713b3efb29001466997de2cf97e8ce6ad0d","observation_id":"c69e52a9-7626-4c08-b9f2-7dc24462842c","resolution":{"observed_at":"2026-05-20T22:39:10.780951Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.09212","last_updated":"2026-05-09T23:14:27Z","snapshot_observed_at":"2026-08-15T01:04:44.428373Z","submitted_at":"2026-05-09T23:14:27Z","title":"Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-12T03:56:28.447136Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.09212"},"observation_digest":"sha256:97a6d0a605fcc0bc67fbaaf2d92522c63826119386f880e35c3b3ca5f766574a","observation_id":"7d449fc2-f463-420e-a44e-d0e961a7747f","resolution":{"observed_at":"2026-05-12T06:51:27.936155Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.12555","last_updated":"2026-05-11T12:00:27Z","snapshot_observed_at":"2026-08-11T20:27:51.560121Z","submitted_at":"2026-05-11T12:00:27Z","title":"DelAC: A Multi-agent Reinforcement Learning of Team-Symmetric Stochastic Games","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-14T21:31:06.105461Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.12555"},"observation_digest":"sha256:70dacf9b6d1628779a193c7eda2c3deb919656546d9f81d85da6d8cf186fe56b","observation_id":"ff9f91cb-790d-4c46-b7d5-bbddcb5401cb","resolution":{"observed_at":"2026-05-14T21:32:59.348331Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.12655","last_updated":"2026-06-10T15:03:44Z","snapshot_observed_at":"2026-08-14T11:53:55.360220Z","submitted_at":"2026-05-12T19:01:16Z","title":"Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning","version":3},"reference_index":123,"source":"arxiv_source","source_observed_at":"2026-06-30T22:11:35.277901Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.12655"},"observation_digest":"sha256:c29bb536f73f3d84be58d34fb29e6300db2170e4a4ac3078562f9f051db42b1e","observation_id":"770d6fbe-f2d8-4eeb-9ff0-e07cbc2b3ea7","resolution":{"observed_at":"2026-06-30T22:15:05.801534Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.18024","last_updated":"2026-05-29T10:46:49Z","snapshot_observed_at":"2026-08-15T14:30:58.732702Z","submitted_at":"2026-05-18T08:14:38Z","title":"Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T18:47:09.501908Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.18024"},"observation_digest":"sha256:5ad821c6653916044542518ad61c3a19c94cb374b996c817cb0e088741ff6b61","observation_id":"e1cb1706-5baf-4eb1-a04f-d320dfcec8e3","resolution":{"observed_at":"2026-06-30T18:55:00.849588Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2605.25867","last_updated":"2026-05-25T13:56:02Z","snapshot_observed_at":"2026-08-15T21:58:16.201656Z","submitted_at":"2026-05-25T13:56:02Z","title":"CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T20:44:11.206380Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2605.25867"},"observation_digest":"sha256:432a10cc20e11cd90cd304b448abd863ca3b441c1f0b5dc6a46b4e65ee547915","observation_id":"c4ee1119-6609-4df4-8ed3-0d9c52a498cc","resolution":{"observed_at":"2026-06-29T21:03:58.925631Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-07-13T07:38:02.368884Z","title":"arXiv preprint arXiv:2006.07869 (2020)","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.14130","last_updated":"2026-07-09T21:57:35Z","snapshot_observed_at":"2026-08-16T00:20:11.928563Z","submitted_at":"2026-06-12T05:30:53Z","title":"Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-13T07:38:02.368884Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2606.14130"},"observation_digest":"sha256:cbba0e79323b99f4e2d83917dfd136c73354f0fa449a6bcd37944e2ca951887a","observation_id":"2b652c66-58f8-49d2-b7ac-07efb65f00d1","resolution":{"observed_at":"2026-07-13T07:38:02.368884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":"2006.07869","doi":"10.48550/arxiv.2006.07869","metadata_source":"arxiv_reference","pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Benchmarking multi-agent deep reinforcement learn- ing algorithms in cooperative tasks","venue":"arXiv (Cornell University)","work_id":"ffcc70b2-bfb7-4e18-a3a9-c4614c3bc974","year":2006},"citing_paper":{"arxiv_id":"2606.25526","last_updated":"2026-06-24T08:05:11Z","snapshot_observed_at":"2026-08-07T08:43:32.910783Z","submitted_at":"2026-06-24T08:05:11Z","title":"Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-25T21:17:28.832301Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2606.25526"},"observation_digest":"sha256:7d61a424158925eade1d786cf73bf60e7b8cc0040c508de75f1045504258c091","observation_id":"e311621e-b17b-4d73-ae61-dd336cf0c76e","resolution":{"observed_at":"2026-07-04T19:30:07.589674Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-02T04:37:28.596016Z","title":"Albrecht","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.13655","last_updated":"2026-07-15T10:02:55Z","snapshot_observed_at":"2026-08-14T15:12:19.969651Z","submitted_at":"2026-07-15T10:02:55Z","title":"Explaining Reinforcement Learning Agents via Inductive Logic Programming","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-02T04:37:28.596016Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2607.13655"},"observation_digest":"sha256:449e24c47e2c5117639458fa4370b0774c8063b2dc1a3a0a184ae3acf6ad154f","observation_id":"fe1c8a65-74ed-4f01-96d4-8a2de63fb823","resolution":{"observed_at":"2026-08-02T04:37:28.596016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-08-01T20:47:24.012667Z","title":"arXiv preprint arXiv:2006.07869 , year=","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.16524","last_updated":"2026-07-17T21:53:07Z","snapshot_observed_at":"2026-08-17T01:57:17.657569Z","submitted_at":"2026-07-17T21:53:07Z","title":"Feedback Attribution and Representation Geometry: Metrics for Comparing Individual and Shared Rewards in MARL","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T20:47:24.012667Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2607.16524"},"observation_digest":"sha256:11593dfd1af9a6d9521804e5ae2d5ea8247b85fb5e94dd42db1890149b65e15f","observation_id":"054b7093-05ad-43dc-ad85-c73cda140206","resolution":{"observed_at":"2026-08-01T20:47:24.012667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07869","snapshot_observed_at":"2026-07-31T21:55:17.265687Z","title":"arXiv preprint arXiv:2006.07869 , year=","venue":null,"work_id":null,"year":2006},"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":26,"source":"arxiv_source","source_observed_at":"2026-07-31T21:55:17.265687Z"},"links":{"cited_paper":"/paper/2006.07869","citing_paper":"/paper/2607.27967"},"observation_digest":"sha256:d2298772527db1c0ba6ea6e812fbb885272018bab5af031e4661a98174dab5f6","observation_id":"d89fa92f-8b2c-4ec2-a17c-90f172b60683","resolution":{"observed_at":"2026-07-31T21:55:17.265687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2006.07869/citation-record","integrity":"/paper/2006.07869/integrity","json":"/paper/2006.07869/citation-record.json","paper":"/paper/2006.07869"},"outbound":[],"paper":{"arxiv_id":"2006.07869","last_updated":"2021-11-09T10:42:04Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T21:21:10.717989Z","submitted_at":"2020-06-14T11:22:53Z","title":"Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2006.07869."}