Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2503.03686.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:09.155947Z
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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.