Paper Citation Record · LEDGER

G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2410.11782.

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

pith.paper-citation-record.v1
2410.11782 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:35:56.974746Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 28aae596-721c-49e4-9ebf-e193d166fa80 · inbound

ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization cites this paper.

ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T22:57:12.819438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5d44be7-2878-4721-9f38-6d0db76ef688 · inbound

CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring cites this paper.

CAFES: A Collaborative Multi-Agent Framework for Multi-Granular Multimodal Essay Scoring G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:39.085570Z

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Observation bb5d62ea-f328-4b90-9dcb-f7d9dd53cdb1 · inbound

MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming cites this paper.

MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:09.640893Z

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Observation cec72ef1-53a1-4642-bbf9-4f79bc6d90af · inbound

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

Adaptive Graph Pruning for Multi-Agent Communication G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:46.099826Z

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Observation 9679fa33-cd61-4063-8840-e6eeaf2d3121 · inbound

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems cites this paper.

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T05:39:59.016741Z

Source-reported events for the cited work

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Observation eab37f38-d52f-44c3-a0c7-5614cae9a874 · 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 G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:15:46.374277Z

Source-reported events for the cited work

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Observation e4edd56d-f9ec-49a4-953c-f24c4b7be12c · inbound

We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems cites this paper.

We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:36.744658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c313ea9-215a-42b9-99bd-da5447f11ced · inbound

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities cites this paper.

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 115

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:51.620414Z

Source-reported events for the cited work

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Observation 023d88d1-aa09-41cb-962b-7611c3a18a2c · inbound

SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents cites this paper.

SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:11:18.184856Z

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Observation a87486a9-6178-49a6-a886-216b62f48280 · inbound

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems cites this paper.

GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T16:32:46.583795Z

Source-reported events for the cited work

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Observation 525660d8-24af-4269-b8ea-866c6bd7e667 · inbound

MASPRM: Multi-Agent System Process Reward Model cites this paper.

MASPRM: Multi-Agent System Process Reward Model G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T07:56:47.964740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebe30f10-df21-4523-a1b1-a866139beb89 · inbound

Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration cites this paper.

Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T00:16:19.305806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2199b55-21fd-4e97-acdc-33e1ccdeb1a3 · 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 G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T23:13:16.017531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5f702a02-dff7-4046-9a87-fe9dd9e6dde2 · inbound

Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models cites this paper.

Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T11:01:43.602781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ec37222-17ac-4c0e-b4aa-27d9fd5f642a · inbound

Transition from Statistical to Hardware-Limited Scaling in Photonic Quantum State Reconstruction cites this paper.

Transition from Statistical to Hardware-Limited Scaling in Photonic Quantum State Reconstruction G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T22:25:15.261445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e17d5c01-5569-47f1-b302-8f9d9fc573d2 · inbound

From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution cites this paper.

From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:05.426829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30e8833b-39dc-4283-9a42-dfd4416a8f16 · inbound

SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology cites this paper.

SkillGraph: Self-Evolving Multi-Agent Collaboration with Multimodal Graph Topology G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T05:25:55.052963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2cf9d7c6-81bd-4230-8085-6a586ec038ba · inbound

Complete Cyclic Subtask Graphs for Tool-Using LLM Agents: Flexibility, Cost, and Bottlenecks in Multi-Agent Workflows cites this paper.

Complete Cyclic Subtask Graphs for Tool-Using LLM Agents: Flexibility, Cost, and Bottlenecks in Multi-Agent Workflows G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T07:06:52.864434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eb106c73-5702-456b-a81d-4045e969a592 · inbound

When Agents Evolve, Institutions Follow cites this paper.

When Agents Evolve, Institutions Follow G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T10:31:30.155096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3dc3631d-cabf-4de3-8a6c-533be41af589 · inbound

Position: How can Graphs Help Large Language Models? cites this paper.

Position: How can Graphs Help Large Language Models? G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:10:43.023937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9767c7fe-2357-48cc-a425-e55cd42d5f6f · inbound

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems cites this paper.

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:51:25.194121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 710389cf-878d-4947-adb6-94fc21351e0e · inbound

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems cites this paper.

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:20:58.689839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 690cd1eb-681f-4fe6-be67-cedf3c5e3c66 · inbound

AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators cites this paper.

AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:56:13.331650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4773ff1f-99b6-4454-937c-8aa822e1672c · inbound

EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems cites this paper.

EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:36:33.429652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0de26752-2155-48c9-aaf3-cc4eb2e4dcfc · inbound

SP-GCRL: Influence Maximization on Incomplete Social Graphs cites this paper.

SP-GCRL: Influence Maximization on Incomplete Social Graphs G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T21:18:00.242691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 98b93ccf-088d-48a9-96b2-6c4e9455c680 · inbound

LEMON: Learning Executable Multi-Agent Orchestration via Counterfactual Reinforcement Learning cites this paper.

LEMON: Learning Executable Multi-Agent Orchestration via Counterfactual Reinforcement Learning G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:18:31.472393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 598843a7-9de1-4981-9231-28dd7556584d · inbound

MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer cites this paper.

MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:38:21.571186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 256d6fc6-f55e-4d39-9f43-12104ca93627 · inbound

MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer cites this paper.

MasFACT: Continual Multi-Agent Topology Learning via Geometry-Aware Posterior Transfer G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-12T16:38:30.296706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58a82ea9-b6d1-4c5c-9feb-0a133da8d684 · inbound

Can LLM Agents Sustain Long-Horizon Organizational Dynamics? cites this paper.

Can LLM Agents Sustain Long-Horizon Organizational Dynamics? G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T17:22:24.368253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5eecfe4d-02f3-49f7-86e7-c8c18213bcc6 · inbound

SIGMA: Skill-Incidence Graphs for Compositional Multi-Agent Design cites this paper.

SIGMA: Skill-Incidence Graphs for Compositional Multi-Agent Design G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T05:49:37.027112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3d900d8-55ed-4d41-b5f3-322d25490dac · inbound

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

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T16:53:40.454538Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 08e414cb-58db-40f6-8bd4-3e8638e19a9c · inbound

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents cites this paper.

MetaPS: Adaptive Programmatic Strategy Selection for Market Agents G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 85

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T08:39:42.648337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f91934d6-b3ef-4cf3-a3bd-332fc01b3ef9 · inbound

MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems? cites this paper.

MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems? G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:59:45.566111Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 20a86fa3-d28b-4358-a379-08567f13ad13 · inbound

Mathematical methods of reinforcement learning cites this paper.

Mathematical methods of reinforcement learning G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 114

Resolution: verified exact; local_arxiv, observed 2026-07-09T22:56:37.760369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 73101d81-90ec-4d9f-9448-eb58521bc7b0 · inbound

Self-Evolving Coding Agents cites this paper.

Self-Evolving Coding Agents G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T19:47:58.978901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fad4e261-e3c7-40d7-b981-5252dda30173 · inbound

ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration cites this paper.

ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-14T04:35:56.974746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.