Paper Citation Record · LEDGER

MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2503.03686.

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

pith.paper-citation-record.v1
2503.03686 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:35:56.944743Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T00:40:51.202109Z

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 58a3d615-6991-496b-9c07-347a86ccee1b · inbound

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

MermaidFlow: Redefining Agentic Workflow Generation via Safety-Constrained Evolutionary Programming MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:09.155947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4a104ff-2598-47a9-8b26-713f04bac4b0 · 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 MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:15:46.355522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de5b64dc-2ab4-496d-a754-806d6a5c2ecf · inbound

SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam? cites this paper.

SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam? MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:02.155182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a343008e-4b4d-46c5-8c33-eb45ebed6156 · inbound

GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis cites this paper.

GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:40:51.204968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c4366f31-51a2-47b9-b6db-c2bb0d2c39ad · inbound

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems cites this paper.

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-05-15T23:21:42.241529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a3e05e0b-4e07-4526-91da-70e52179fbad · 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 MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-14T23:13:16.379939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 855d5a95-c4e9-4791-b7ef-eb4a07c6c1f3 · inbound

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation cites this paper.

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T03:59:46.814740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82b7896d-eb37-41c0-a1a2-14ee0c7170a7 · inbound

Improving Role Consistency in Multi-Agent Collaboration via Quantitative Role Clarity cites this paper.

Improving Role Consistency in Multi-Agent Collaboration via Quantitative Role Clarity MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T20:13:12.982526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3dfc4573-9774-43c7-a363-bee80deb8f61 · 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 MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:06:52.845339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ca6fd66e-410a-4964-9727-130e3c3bb54b · inbound

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

ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-14T04:35:56.944743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.