Paper Citation Record · LEDGER

Learning Multi-Agent Communication from Graph Modeling Perspective

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2405.08550.

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

pith.paper-citation-record.v1
2405.08550 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:17:51.396797Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T19:45:00.796757Z

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 c8dd30d5-1e51-479e-8e20-6bc5e49f5685 · inbound

TACTIC: Task-Agnostic Contrastive pre-Training for Inter-Agent Communication cites this paper.

TACTIC: Task-Agnostic Contrastive pre-Training for Inter-Agent Communication Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T22:17:51.396797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70abaccb-c082-45ea-ae31-19ff9d009933 · inbound

Adaptive Graph Pruning for Multi-Agent Communication cites this paper.

Adaptive Graph Pruning for Multi-Agent Communication Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:43.452061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 889e1e37-cbb7-43dd-8f59-2262fe57b691 · inbound

Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models cites this paper.

Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 12

Resolution: malformed identifier; arxiv_id, observed 2026-05-21T20:40:35.704192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 959c474b-43b1-4719-82e8-27433ee1c5db · 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 Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 53

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:13:15.991080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c5f21297-d7a3-4179-b7cd-ebb06cf7740d · inbound

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models cites this paper.

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 92

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:53:42.225810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a9416eb8-148a-4f6b-bae5-07b9661f04e8 · inbound

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models cites this paper.

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:45:00.798275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef937e85-8da5-4794-a68f-fc36ccd9c71c · inbound

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning cites this paper.

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:13:13.670425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 96ecc33a-f7fb-4fb3-8c41-a32c77097f39 · inbound

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning cites this paper.

LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T19:15:01.218050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f2dcf540-343f-4b1f-aca7-7f02a4283b81 · inbound

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate cites this paper.

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate Learning Multi-Agent Communication from Graph Modeling Perspective

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T16:53:40.441971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.