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Paper Citation Record · LEDGER

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2212.07489.

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

pith.paper-citation-record.v1
2212.07489 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:50:49.787330Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:47:18.874078Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation be604130-52a3-480a-b62e-408e824ae902 · 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 SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-08T18:50:49.787330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T18:50:49.787330Z digest=sha256:3cc1d872b2c38005af8ce1d6c3d27e63909943257f63b897733657029dfcfd75

Observation 394f1c3e-d73e-48ac-9497-39555c94c967 · inbound

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

Light Aircraft Game : Basic Implementation and training results analysis SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T00:23:07.425085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:23:07.425085Z digest=sha256:057575e5daff19407dadfe768f5a7421d8d87dfeb49bbca63fcb496c76e8dfa2

Observation b9cf3c3c-8c82-4afe-835d-d8c8b7442702 · 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 SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T17:57:08.282307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:57:08.282307Z digest=sha256:e8821d529434ead189a25140fb73361fee593ef2c1a8d23c948e7b5d5ab4a48d

Observation 06285515-2a5a-4902-8f8a-d1abc47af237 · inbound

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

Play Like Champions: Counterfactual Feedback Generation in Latent Space SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-07-02T19:47:18.876157Z

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=sha256:27bb1f9349856d0859a9f5a103b0a9b5c73206bc1fed4e7ae92602bf0447258b

Observation c87bc531-43ab-433f-a7b1-bd217364ef5c · inbound

Feedback Attribution and Representation Geometry: Metrics for Comparing Individual and Shared Rewards in MARL cites this paper.

Feedback Attribution and Representation Geometry: Metrics for Comparing Individual and Shared Rewards in MARL SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-01T20:47:23.233769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T20:47:23.233769Z digest=sha256:0aaf27d0248b4d477456c03a940e40ee9013dd8d2ff4b91d8e28f1b3e6bd0a32

Observation 4bcbad1a-ce27-4c67-9e50-ac4cf8d5ab1f · 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 SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-03T04:15:05.572715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:15:05.572715Z digest=sha256:3c046641af1a49b46fd0b5fb0a94a485c541028aa8e8b35f947569a4ba84f407

Observation ccfde3ed-aee8-483f-ba6e-a5bd5085bef3 · inbound

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-05T15:25:39.630031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T15:25:39.630031Z digest=sha256:6a894b10f163f8a0035e40de1d10df0babfdbc14aefc409e658a29f3173b579b