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Paper Citation Record · LEDGER

A Survey of Multi-Agent Deep Reinforcement Learning with Communication

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2203.08975.

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

pith.paper-citation-record.v1
2203.08975 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:57:41.704691Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T18:48:50.031401Z

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 3460f3f8-c3ed-4451-a06e-7c12047ed3f2 · inbound

Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication cites this paper.

Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-23T03:35:20.710212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T03:34:58.917609Z digest=sha256:87d97d9d0798a6d8d7aa7261bf9cad00f7b60aa3645038af5a178d037e6519d0

Observation c3ff40aa-7006-499b-8ddf-9d857b102c3f · inbound

Verbalized Bayesian Persuasion cites this paper.

Verbalized Bayesian Persuasion A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 134

Resolution
unresolved
no resolver link, observed 2026-08-09T14:57:41.704691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T14:57:41.704691Z digest=sha256:423540c52a721b604c55f3f3e71e056b951d79a28a5ad4f250db274339c1c44c

Observation 8d1c2dfa-24a9-42f0-957a-f58a5a2eb6d3 · 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 A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 179

Resolution
unresolved
no resolver link, observed 2026-08-06T17:57:08.797964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:57:08.797964Z digest=sha256:318ba9ceb39c0843e6cef9a837b6234c4cd00f5663e62abf46ba5d977f878e77

Observation da5f9b64-f5c5-4699-abe8-0ca579cd6402 · inbound

Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence cites this paper.

Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-06T15:53:46.945532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:53:46.945532Z digest=sha256:0e0feadf6a93ff23555ad36221ad01858be0c15b5d681078bfe528afbb70b750

Observation 8a5b9752-e166-4ee3-9e24-4ea633cc7071 · inbound

Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks cites this paper.

Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:55:58.963006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:55:19.978358Z digest=sha256:eb8857ea099277e4f323a952a4fdc043e9201714e9860cb64d55a97b325fbca4

Observation 2906ed1e-d670-4291-b1bb-1545c5f3da00 · inbound

SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks cites this paper.

SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-06-29T11:43:23.566186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T11:41:30.827427Z digest=sha256:c90630283094f2e1643de013bd1956ab5361295b76c855e002033a1b6b32cb93

Observation 7d9e19c0-2333-487a-b306-59a3c422be99 · inbound

Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning cites this paper.

Cooperative Long Rope Skipping via Multi-Agent Reinforcement Learning A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T21:27:24.463466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-27T19:46:31.662942Z digest=sha256:fff6695c0dfe336e6e2fee3a01f00591307b85a50d861ee662435e2b063f6f65

Observation 18a9f565-ce26-4ae0-bec5-688041647d4f · inbound

BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning cites this paper.

BARD-MARL: Byzantine-Agent Detection for Learned Communication in Multi-Agent Reinforcement Learning A Survey of Multi-Agent Deep Reinforcement Learning with Communication

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-03T18:48:50.032662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-27T02:18:55.535721Z digest=sha256:960cedfc45cb6a7d962ecc18719ee14861e67ed07c5f17ae58d656f42ffd9115