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Paper Citation Record · LEDGER

The StarCraft Multi-Agent Challenge

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 59 inbound Pith citation observations for arXiv:1902.04043.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1902.04043 v5

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 59 of 59 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:50:49.863497Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T19:30:07.613613Z

Reference resolution

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Outbound references

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Pith citing papers

Observation 39e91012-e2a6-485f-b63d-344279a0fbc2 · inbound

Arena: a toolkit for Multi-Agent Reinforcement Learning cites this paper.

Arena: a toolkit for Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 17

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arxiv_id, observed 2026-05-24T18:36:19.039203Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-24T18:35:31.633881Z digest=sha256:1b06ac527c8a33eb2a5c10978f76a5b6c2d69a77382a68c5f7664544f95fc6c3

Observation 284ff8e2-15cf-4a6a-bf1c-96c88f797993 · inbound

Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning cites this paper.

Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 19

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arxiv_id, observed 2026-05-24T02:18:44.775808Z

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source=arxiv_source observed=2026-05-24T02:18:32.308551Z digest=sha256:43771f1e1fc0b7f90266e143967404f4aa1a284e66e6d79093758d7fd2deacc6

Observation 0eb12ab9-d1e3-4523-9fa6-f5f09fd462bc · inbound

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning cites this paper.

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 13

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arxiv_id, observed 2026-05-23T03:25:20.389635Z

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source=pdf_text observed=2026-05-23T03:24:33.788346Z digest=sha256:71dca9e1803111be6166aec7411d130a74706c7daab7f2f769a0913ae51c46fd

Observation 5e9f1047-0c1b-4787-80cc-b805414b23a1 · inbound

Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning cites this paper.

Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 19

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arxiv_id, observed 2026-05-23T04:17:30.922080Z

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source=pdf_text observed=2026-05-23T04:16:54.807723Z digest=sha256:92368a417b454a122bd826da14a885cbf32426c81c89c5dfa81d6ffb052474e9

Observation 257e8abb-3686-4428-a0ae-01de21d44260 · inbound

Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning cites this paper.

Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning The StarCraft Multi-Agent Challenge

Reference 29

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source=arxiv_source observed=2026-08-08T18:50:49.863497Z digest=sha256:b84e4423159c9054d3b59e66e2903bd300b064572c758da714bce637f96253fa

Observation c1ee0937-98a4-4b67-8e05-ba468c39f13a · inbound

Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners cites this paper.

Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners The StarCraft Multi-Agent Challenge

Reference 41

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source=pdf_text observed=2026-08-07T23:14:16.504684Z digest=sha256:323a4f4d4e05d6f8d845c919f5c4836267435e01bba3aeac27c80f41bbea4122

Observation 3457c812-1931-411a-8efd-88b8c4daf435 · inbound

AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit cites this paper.

AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit The StarCraft Multi-Agent Challenge

Reference 22

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source=pdf_text observed=2026-08-07T20:39:51.742369Z digest=sha256:727cdc31ed380e31f6e635802721f9c90f9ed194e05aee708ffe5b682c22146e

Observation 69770212-85d2-4c43-9cde-adfd7935c680 · inbound

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs cites this paper.

Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs The StarCraft Multi-Agent Challenge

Reference 22

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source=pdf_text observed=2026-08-07T14:16:46.575546Z digest=sha256:5631f92c8bf46f835bbd66913760e8e54ed9af90f5b54f0510f30d12138c912d

Observation 92c7db2b-d470-4bae-89d4-e589f727fc66 · inbound

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications cites this paper.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The StarCraft Multi-Agent Challenge

Reference 55

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source=pdf_text observed=2026-08-07T14:10:43.252614Z digest=sha256:fcec989e882a506790361b3717d82442435503dd03453f6aefe48564e8f6b668

Observation a06817dc-94c0-40e9-b3b1-aced4a8a7c3e · inbound

Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games cites this paper.

Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games The StarCraft Multi-Agent Challenge

Reference 81

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source=arxiv_source observed=2026-08-07T13:10:24.777756Z digest=sha256:14f0ccd743070620a782d73cb5d65a7bba24d477e2422bf4d9b3c2082454265a

Observation e963249d-7e11-47ae-b019-9e3359cf0bd2 · inbound

Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning cites this paper.

Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 43

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source=pdf_text observed=2026-08-07T12:09:12.878586Z digest=sha256:04c88f8041fbabaa529c446455a52efcb97b57f13e06dc8386e35afca7c89ab6

Observation afac9e65-3dd0-4a7c-a5a1-7f4bb1fad6d0 · inbound

VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments cites this paper.

VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments The StarCraft Multi-Agent Challenge

Reference 60

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arxiv_id, observed 2026-05-19T11:57:16.298923Z

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source=pdf_text observed=2026-05-19T11:57:08.314088Z digest=sha256:78129f255750a5f954c6b37bbd9f343799715dba3d1a7e31ebe6d83a3f359f49

Observation ecc5c207-bda1-405e-96f9-fc50c19aaaeb · inbound

Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage cites this paper.

Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage The StarCraft Multi-Agent Challenge

Reference 14

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arxiv_id, observed 2026-05-19T11:12:15.527732Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T11:08:51.426575Z digest=sha256:27398c4d89b849aea67d4b6a4649be8aa85a62cdb3789568cf032329a5deffa7

Observation 40197884-7218-4cbc-9eda-ab5aeab03682 · inbound

Light Aircraft Game : Basic Implementation and training results analysis cites this paper.

Light Aircraft Game : Basic Implementation and training results analysis The StarCraft Multi-Agent Challenge

Reference 9

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source=pdf_text observed=2026-08-07T00:23:07.922367Z digest=sha256:6ea160ba875c72f8005d5c6104df9ea021b10d692fb2424c08eca54994aca2e8

Observation fe880bbb-6615-47ed-b8f3-1c3c36d30eb5 · inbound

MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning cites this paper.

MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 36

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source=pdf_text observed=2026-08-07T00:16:45.835286Z digest=sha256:46b5472842333b38a4ab4af055dc6704a0f918b737cbbf72fe777b9efd058ef7

Observation a18f12c5-0726-4c6e-8f19-7a0886f2bcf2 · inbound

Focusing Influence Mechanism for Multi-Agent Reinforcement Learning cites this paper.

Focusing Influence Mechanism for Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 61

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source=pdf_text observed=2026-05-19T07:16:37.438901Z digest=sha256:dbf70821224d207535bb3841c096ca8bd87dc958cf7113c90122eb1a12f6c30c

Observation bc81f89e-a19b-4baf-ac9f-ab4709027df4 · inbound

Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning cites this paper.

Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 20

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source=arxiv_source observed=2026-08-06T23:02:24.663940Z digest=sha256:65a3fe874371ca21c61fd25fd22cb0cc4bfeb790c77f5062fa3f6ab26e7b5907

Observation 3724e73d-6be2-4af4-8957-7a144c55dbcc · inbound

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals cites this paper.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals The StarCraft Multi-Agent Challenge

Reference 19

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source=arxiv_source observed=2026-08-06T20:59:15.116702Z digest=sha256:4df861a9a9c3cab49a11a05d82c09c55b1649d7ec1b81653e153871d517a0857

Observation 79a94b4d-7fc6-4bd3-a3b5-763891662f2d · inbound

From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination cites this paper.

From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination The StarCraft Multi-Agent Challenge

Reference 25

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source=pdf_text observed=2026-08-06T19:17:11.193353Z digest=sha256:cc001584dc1bc5c9318fcfcc0d0b1e7986fa8f5e26f505f5e2ca99a6d196a669

Observation 48c7d419-b193-43aa-9759-d9236b8224f5 · inbound

Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning cites this paper.

Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 2019

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source=pdf_text observed=2026-08-06T18:58:35.903637Z digest=sha256:6376523b81d253329001fdf4669d81004d27945457551942142a3e62074c9956

Observation 1ba737ca-6aef-4d50-b7e0-b265ad8f5dea · inbound

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley cites this paper.

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley The StarCraft Multi-Agent Challenge

Reference 30

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source=pdf_text observed=2026-08-06T18:43:23.443674Z digest=sha256:fa0e8e216d9645b9b192a5dc6ca6b3ad48ca95d0f0d558e9908488ba6707828c

Observation c8538fe2-3663-4873-becf-c54e88cdf54f · inbound

GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective cites this paper.

GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective The StarCraft Multi-Agent Challenge

Reference 132

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source=pdf_text observed=2026-08-06T17:57:08.618710Z digest=sha256:79a9796f9530dd0f1b6374f31581283bca9615eb87939bfad42fc35b86142050

Observation e9bd22fd-2bb8-446d-990b-08be171f2651 · inbound

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review cites this paper.

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review The StarCraft Multi-Agent Challenge

Reference 87

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source=pdf_text observed=2026-08-06T17:42:56.131650Z digest=sha256:da02524a142d5d662cb5c1741cffb3dfa835cfb2b41a1f2ef4a8be37c9bdf2b8

Observation ef66e1c3-d6dc-4a40-a18f-1987bb5038a1 · inbound

Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics cites this paper.

Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics The StarCraft Multi-Agent Challenge

Reference 2019

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source=pdf_text observed=2026-08-06T12:38:48.099267Z digest=sha256:df1357990abcc56d4a8e60f12ac4a7a38fbf26a46ff0fb11c9782b5d34a1e33f

Observation 600f0899-3509-4790-b3be-85fc39f1eaa4 · inbound

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning cites this paper.

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 70

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source=pdf_text observed=2026-08-06T10:40:38.849091Z digest=sha256:81e09a104d7d05437c2bc9b4a0c4fce49ffbc361f1aa5b4d927e9cc5a93f478f

Observation 0fb317f2-1174-470a-a596-89e4fb743e01 · inbound

SC2Arena and StarEvolve: Benchmark and Self-Improvement Framework for LLMs in Complex Decision-Making Tasks cites this paper.

SC2Arena and StarEvolve: Benchmark and Self-Improvement Framework for LLMs in Complex Decision-Making Tasks The StarCraft Multi-Agent Challenge

Reference 16

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source=arxiv_source observed=2026-08-05T20:30:30.115627Z digest=sha256:d024e3c23ee287c8edcdf895e00823d1743ed622eb98404b1425c9939b243705

Observation 3c993876-5d60-4289-a74a-7c76678fc352 · inbound

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending cites this paper.

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending The StarCraft Multi-Agent Challenge

Reference 37

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source=arxiv_source observed=2026-08-05T14:49:53.303698Z digest=sha256:59c33cb1877cb6319a5600f7bb3ad388bff38e5f640417117fbcc2fd83d1b4c7

Observation 5306b4e4-2b89-4618-b7ec-3ee871a64e7c · inbound

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games cites this paper.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games The StarCraft Multi-Agent Challenge

Reference 46

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source=pdf_text observed=2026-08-05T10:51:55.765630Z digest=sha256:fc331542b356aeb311cf17740f3fc572ed039edf2d344f1bbf551356e2dc3235

Observation dae82299-ddee-404e-9759-ccb29770d2fb · inbound

PillagerBench: Benchmarking LLM-Based Agents in Competitive Minecraft Team Environments cites this paper.

PillagerBench: Benchmarking LLM-Based Agents in Competitive Minecraft Team Environments The StarCraft Multi-Agent Challenge

Reference 16

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source=pdf_text observed=2026-08-04T23:58:45.783516Z digest=sha256:b279e884fa6256d4687e14b9dac20742ad422284114cfb9e98723e65264acb70

Observation 96a1ab45-e253-4f3b-8e81-698add98763f · inbound

Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration cites this paper.

Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration The StarCraft Multi-Agent Challenge

Reference 42

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source=pdf_text observed=2026-08-04T17:49:15.036493Z digest=sha256:45030ff0fefac228bba51604aaaed70776158357cc17c5a6712b35f1d2f243c1

Observation 2244432f-dd46-46f0-81db-08e69517363e · inbound

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem cites this paper.

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem The StarCraft Multi-Agent Challenge

Reference 20

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arxiv_id, observed 2026-05-18T15:16:32.282503Z

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source=arxiv_source observed=2026-05-18T15:15:26.114479Z digest=sha256:4371e538cec36735fca9b7dc0c60fbb14c494e91e356ed2bb5f33181649acb53

Observation c6b7d9f4-8577-469a-bbbc-eaf44d491776 · inbound

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.

Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory The StarCraft Multi-Agent Challenge

Reference 106

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arxiv_id, observed 2026-05-14T23:13:15.710370Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-14T23:13:15.016486Z digest=sha256:6158913cfc8962e298585d5fcfdad048bb80b25b2528fb34975a0a2c57941d42

Observation 0787dc86-5d0d-4bc9-a240-30d91f99c5ac · inbound

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning cites this paper.

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 14

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arxiv_id, observed 2026-05-25T07:45:29.787490Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T07:41:48.634351Z digest=sha256:0dfc90c944ec3dc0584d9e9e85fb662ce71b98c585c92166e4207fe28c259f1e

Observation d0261826-d566-40ab-8471-3e1d8d24f236 · inbound

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning cites this paper.

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 2016

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source=pdf_text observed=2026-08-03T05:37:38.464277Z digest=sha256:1513e6cbfbc95476dee2d67df58ca758e8dce6770b6d0ba2fe439e1f3fbc2b71

Observation 998ad793-1996-41c2-813e-3345858c916a · inbound

Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning cites this paper.

Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 37

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arxiv_id, observed 2026-05-11T08:05:58.890142Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T16:50:51.653571Z digest=sha256:c3b59e00d7c79f31e2803acc625c8c83edbc0d3471691b08468a92792adb8076

Observation 86872182-772a-4be0-a1e8-ee08bae212b7 · inbound

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning cites this paper.

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 50

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arxiv_id, observed 2026-05-11T07:41:01.310875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T17:01:35.037646Z digest=sha256:47ca940a1b8b52fe31972d2c3db1514e3c984752f2132919cd004596cd76a510

Observation 9331e6ad-6ac9-4f07-a09a-3efc9299d84f · inbound

Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents cites this paper.

Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents The StarCraft Multi-Agent Challenge

Reference 40

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T19:26:08.787056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-08T12:00:07.345611Z digest=sha256:880df16e5d0d2b049c9338d239154ff93582085456598ea4f366b7f805fdc214

Observation 2a7b9eee-34b2-4caa-ab47-d3ff6bd9e092 · inbound

A High-Throughput Compute-Efficient POMDP Hide-And-Seek-Engine (HASE) for Multi-Agent Operations cites this paper.

A High-Throughput Compute-Efficient POMDP Hide-And-Seek-Engine (HASE) for Multi-Agent Operations The StarCraft Multi-Agent Challenge

Reference 25

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T09:36:26.196097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-07T10:27:55.337253Z digest=sha256:c7d67245e1b7f593831195e3140659ce97213c2d860d0dcf02a28afc76760a37

Observation 24d67899-6ecb-427b-9f4f-d3a376f8c767 · inbound

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making cites this paper.

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making The StarCraft Multi-Agent Challenge

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:56:07.283260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-09T14:29:18.385955Z digest=sha256:75ebc4480ea5fb6765a331fabdc13c1f2a5f8c6d35d5f60eb229ff014511d8d5

Observation fde79c3c-229b-4d17-92da-b7e8025aef56 · inbound

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making cites this paper.

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making The StarCraft Multi-Agent Challenge

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:27:29.622030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T07:26:08.192587Z digest=sha256:55cf0cb4f329adfa2165dc91c7fea67175a73366d7fb3251b5cca26453d14efd

Observation caf11c4b-3005-45a3-9645-5209139721e8 · inbound

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making cites this paper.

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making The StarCraft Multi-Agent Challenge

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-14T21:29:28.805783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-14T21:28:28.236225Z digest=sha256:113867f42dea90e2e978d5470a5444c60097d3ce79dae52cdc09f004ba44199c

Observation 3141423a-4945-4eac-bc25-b40284f0b65b · inbound

Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization cites this paper.

Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization The StarCraft Multi-Agent Challenge

Reference 31

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T00:51:14.843828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T00:50:35.026779Z digest=sha256:130da645d6406be9a00320e2b95036beafd8eb479ebc7ce2c0551a5848f9d63f

Observation d5b27676-c461-4702-b8d7-452b5b790a6f · inbound

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning cites this paper.

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 52

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T07:56:27.332574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T01:31:19.576223Z digest=sha256:d74460384374cc59c997ad38129a5bba6dd4e492dfc0e4b149bfc2bc071c0d3b

Observation 6a77c3c6-f53b-4096-a534-22edce3e9223 · inbound

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning cites this paper.

SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 52

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T22:39:10.823818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T22:34:46.440511Z digest=sha256:c96e7d7cba726a3b44a2c3ecf8d4c2cfe797684adb5330fefc92cd796f78c990

Observation e2300159-f291-4617-a1de-5847ee34bd17 · inbound

Beyond the All-in-One Agent: Benchmarking Role-Specialized Multi-Agent Collaboration in Enterprise Workflows cites this paper.

Beyond the All-in-One Agent: Benchmarking Role-Specialized Multi-Agent Collaboration in Enterprise Workflows The StarCraft Multi-Agent Challenge

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:06:37.826663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-12T03:41:40.370052Z digest=sha256:60944b1d9281e87d30867b6e4030d8ab54a2e54d907fa66ca7769f10dc65cf41

Observation 91b12c73-6ffd-4515-85f2-e0d76eb6bb4f · inbound

Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming cites this paper.

Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming The StarCraft Multi-Agent Challenge

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-19T15:27:38.640395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T15:27:22.127896Z digest=sha256:e62af9dd1ceabf14b530d923ff1bc83713d3ed947672daec252f57eb02bdcee7

Observation 88322232-2290-4bed-b9e5-7ea08614a113 · inbound

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning cites this paper.

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T12:23:16.763125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-20T12:23:11.320751Z digest=sha256:fef48cb0913df4f5425e0762332fdbee17c642c9e13863e5f69624a169704dcf

Observation cd91a27e-7eb2-4d1f-b01f-796541f7b85f · inbound

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning cites this paper.

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T18:55:00.840620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T18:47:09.501908Z digest=sha256:481cacbaab48e0e1cfd363e06e82e3e44edc1eda34251dbbc2b29ef6c21473e4

Observation fb35782f-56fa-4cca-a410-c52663e1974c · inbound

Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation cites this paper.

Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation The StarCraft Multi-Agent Challenge

Reference 128

Resolution
metadata mismatch
arxiv_id, observed 2026-05-20T12:48:17.512475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-20T12:44:29.147095Z digest=sha256:ee476c4111475c9009285467689139ade11d3dc827795e289382c58b8a9cbd2c

Observation 2b55a951-c3d2-470f-a251-26011fb763fa · inbound

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning cites this paper.

PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:26:38.686813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-25T05:26:22.623367Z digest=sha256:8d853c5b07fdd0dd62b5fa728b65cf81dff8c6b44495163906db533a3d669a8b

Observation e6de946c-ea2d-4667-b43e-a097b14e3954 · inbound

Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning cites this paper.

Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-02T07:26:45.890958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T06:57:36.238274Z digest=sha256:1c3da2d213b005dfaa8b6d98badc5add0c03abeed7c0a9a0c78f8126e22c2c01

Observation 7af2a22e-5a59-4af2-a01b-8dc27c6a6d73 · inbound

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria cites this paper.

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria The StarCraft Multi-Agent Challenge

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T08:47:50.460561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T10:42:17.852996Z digest=sha256:a004c0bc1defc241c25d9b276ba9fcbc44039f8231070a70d81a8d8c82d09de5

Observation 8bc1d099-97b3-445c-84d7-48b9a387282c · inbound

CCKS: Consensus-based Communication and Knowledge Sharing cites this paper.

CCKS: Consensus-based Communication and Knowledge Sharing The StarCraft Multi-Agent Challenge

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T13:48:20.891538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T07:33:50.358196Z digest=sha256:fc2a82ea0fcf222cef321fba950f1a1577d7d0a6b1ba3d53946d4714479c3d45

Observation 174f807c-1309-479d-825b-eb1a14661109 · inbound

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning cites this paper.

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-07-03T19:18:54.942471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-27T01:54:19.216553Z digest=sha256:e7fa4169711ea0a6784e721b0fc27245e676f7f9f04b525720370db1398f12c0

Observation e7571810-32ff-4db3-b13b-c10dd098412a · inbound

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning cites this paper.

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-07-04T17:20:00.541732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-25T23:46:50.183064Z digest=sha256:22d0741104f90e436af9a283c4ada96a9b548a394b5258401345dc5db187ae9c

Observation 9395ea3d-2d17-4909-b2b8-ddebff0f6c08 · inbound

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning cites this paper.

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning The StarCraft Multi-Agent Challenge

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T19:30:07.614931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-25T21:17:28.832301Z digest=sha256:1bde8e5d4131c2833d0ce0c7f0cdb2aed1572f72536de6bb72834e22772c5d1e

Observation 1b8c8b35-de31-45c9-b704-4af13e4c561e · inbound

Play Like Champions: Counterfactual Feedback Generation in Latent Space cites this paper.

Play Like Champions: Counterfactual Feedback Generation in Latent Space The StarCraft Multi-Agent Challenge

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T19:47:18.856152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-02T19:41:39.901829Z digest=sha256:0bb85560fbdda90f6ddf5139b1d7866a54fbdf6e668e8cc84c92fbefceedff65

Observation f29c1eb2-9923-48e6-9b69-b4c50c397dd7 · inbound

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents cites this paper.

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents The StarCraft Multi-Agent Challenge

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-01T14:36:00.853580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:36:00.853580Z digest=sha256:38c838000fbe3a99f70d4ef4b7eff19eee5dc9b4e3a401b787aabaeb04e09f68

Observation e7bac09c-82ec-40ea-aff7-408267d2403b · inbound

DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat cites this paper.

DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat The StarCraft Multi-Agent Challenge

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-03T04:15:06.067394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:15:06.067394Z digest=sha256:3f6cf686800f384902dbe634ee1228cfe56bc796653f63249512db7af92cd5b3