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Paper Citation Record · LEDGER

LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2405.11106.

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

pith.paper-citation-record.v1
2405.11106 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:15:46.313451Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ffd589cf-8436-4a2b-a609-4c1ba1740507 · inbound

Large Language Model-Brained GUI Agents: A Survey cites this paper.

Large Language Model-Brained GUI Agents: A Survey LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-19T11:08:28.017293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T11:08:27.472508Z digest=sha256:fe743b96bffd8ccbff369c38f2a366a73c9c745ebe0d28cf80d2f82eac824f51

Observation 61f93453-9e4e-4792-a08a-e298c07da7a6 · inbound

Multi-Agent Collaboration Mechanisms: A Survey of LLMs cites this paper.

Multi-Agent Collaboration Mechanisms: A Survey of LLMs LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 118

Resolution
verified exact
arxiv_id, observed 2026-05-13T15:54:54.272152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-13T15:54:54.146003Z digest=sha256:8d53cdcbcc83afa71601b8c372a43b8f228fc83c3cd89576e091be0dbba552e9

Observation 6dfa02b4-64d2-4034-907e-24d1ff83c84a · inbound

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning cites this paper.

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T05:15:46.313451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:15:46.313451Z digest=sha256:eef67bc5546317dce6dcad5d1d2e84b021dc3d19e7ae7e1d055a974d6f746583

Observation a1f76433-9b2a-45fa-8d74-ed3c8261d4c1 · inbound

RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms cites this paper.

RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T21:02:33.207487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:02:33.207487Z digest=sha256:278361a310d8d406d1d86accc34c63eddcbcc954bac0819ce59894f86cbfefa5

Observation 003568f4-bc90-4abd-ba59-6c8b11e9ba29 · inbound

WebSailor: Navigating Super-human Reasoning for Web Agent cites this paper.

WebSailor: Navigating Super-human Reasoning for Web Agent LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-17T15:37:09.712100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-17T15:37:09.572241Z digest=sha256:2095b7e657640039ca604e3e8662044a3e2bbf974d3145a2e9d96ffe6b481258

Observation 85482870-44a4-4f09-822f-316d2cf3f20c · inbound

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence cites this paper.

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 125

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T22:23:15.856569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-14T22:23:14.621091Z digest=sha256:bf81c81bb71c737b8d30424768b36ca97285becc5486ee2f91f6bc30b7dcf411

Observation fabc8d4a-808d-47a5-a9f6-17af5f205a5f · inbound

LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning cites this paper.

LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-05T13:46:02.788254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:46:02.788254Z digest=sha256:8493c8279e214dcd543ae5c9d3b0fe3f0519f16e4b8d271f5541e743e759c232

Observation b6f36d5c-56ce-427c-b255-cd2b859afd21 · inbound

A Survey of the State-of-the-Art in Conversational Question Answering Systems cites this paper.

A Survey of the State-of-the-Art in Conversational Question Answering Systems LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-05T05:11:23.150380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:11:23.150380Z digest=sha256:68c2c63d6510bb1d29152b982a1e9e43333f555404e3441d8dacd36ecab3e9b7

Observation 372fa8f8-d5dd-4ae9-9150-6eac6627c569 · inbound

Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming cites this paper.

Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-18T06:41:00.563951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-18T06:38:24.695002Z digest=sha256:ea4d6f3e2e645632e2230a22622349c174160d0d0cfe8bb728b0ed504ac62428

Observation a79c6109-ad03-40be-85dd-c06562f1f2e7 · inbound

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents cites this paper.

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T18:33:15.280434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-16T18:32:01.569665Z digest=sha256:212134ce0735877fea1980d0fb054171e37f3be0cb9a2fd17dd4349d2aaf2fd8

Observation bb1d87e6-2825-4d45-9ca8-e2197dcb73b7 · inbound

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents cites this paper.

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T12:45:05.696276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T12:45:05.696276Z digest=sha256:ac225330e44f20dfa8ca317d98d32af2e55f8e82309d1d58392c2fc556c723f9

Observation 45508f44-d710-45e9-bd5d-5a2d02df45e5 · inbound

Joint Optimization of Multi-agent Memory System cites this paper.

Joint Optimization of Multi-agent Memory System LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:15:34.604330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-15T12:12:35.056095Z digest=sha256:6b2899540c976ea8e95bd6cfc14400baa42f9b9396082f8d08e4dcac58675364

Observation 397440b8-dd14-462a-a9ab-5249f6538543 · inbound

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution cites this paper.

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-10T09:08:27.138711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T08:06:29.467987Z digest=sha256:dffbb839a056dd101ab7ba95e2dad3caf5f155fd7cc411dc09e7cac277f1b406

Observation b6176c7e-de33-4803-9b7a-c001c63ab2b5 · inbound

Do LLM-derived graph priors improve multi-agent coordination? cites this paper.

Do LLM-derived graph priors improve multi-agent coordination? LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:11:20.213591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T06:09:08.100187Z digest=sha256:8a4139c57520cbdccd69cafb5088dc301e146af7ce56b6fcec58dc33c9414da8

Observation 1a223834-cc8a-4e71-b107-2c9564581554 · inbound

Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures cites this paper.

Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 131

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:51:03.815274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T04:31:28.242097Z digest=sha256:b78526923db25b1956d903040d1aa79a1d1aec1c164516caa3a9513b0542be86

Observation 74ac8ecf-caab-4893-8287-bfa7872dd406 · inbound

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning cites this paper.

Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 48

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T22:15:05.737340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-30T22:11:35.277901Z digest=sha256:d5011239ca7aa938e477baf848402921ead7d182b3f13d81084a1d942cc25223

Observation 153ca3c2-f869-454f-ba54-cb13aa871ed0 · inbound

ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning cites this paper.

ReCrit: Transition-Aware Reinforcement Learning for Scientific Critic Reasoning LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:53:49.364775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-20T22:51:56.666980Z digest=sha256:7e53878ac5b511ba86358c9529840ce659c7f1467848bbe80a945ea4f2597cf4

Observation 71033bfd-bc23-4bd3-b91e-9ef02af06b6d · inbound

Multi-Agent Coordination Adaptation via Structure-Guided Orchestration cites this paper.

Multi-Agent Coordination Adaptation via Structure-Guided Orchestration LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T19:43:54.922885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-29T19:36:42.234848Z digest=sha256:02a96c15d49c3364b86d16c57101adf521527c73d9555e94cd21121bee249fd4

Observation aae5e59c-699b-44fc-801a-cdd5a662ea25 · inbound

Deep-Unfolded Coordination cites this paper.

Deep-Unfolded Coordination LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-04T04:19:34.375856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T17:05:44.372648Z digest=sha256:13efbf2d9ce5770a0d39e36610c2bfcc358bac7483752fb720250d0341efa3b3

Observation 8f191f63-7402-4817-8025-0b843c6c6b89 · inbound

Reason Before You Retrieve: Agentic Planning for Multi-modal RAG cites this paper.

Reason Before You Retrieve: Agentic Planning for Multi-modal RAG LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 131

Resolution
unresolved
no resolver link, observed 2026-08-02T10:20:56.162142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T10:20:56.162142Z digest=sha256:0c7bdcd33e757416a3a1c1b2ccf8c1ed2e6cc61dcee2c4d9d75a7ba2e8696db9