arXiv — cs.AI preprintsInternational15 October 2026
MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.17613v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) can solve complex problems beyond the reach of a single agent by decomposing tasks and coordinating complementary capabilities. Despite extensive research on MAS, existing work largely focuses on specific aspects or individual phases of their development and operation, resulting in fragmented perspectives with objectives and signals that are difficult to reconcile across these different settings. To bridge these disconnected perspectives, we conceptualize MAS development and operation as
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence15 October 2026
- The Silent Failures in Agentic Data Science15 October 2026
- ClawLoop: Self-Improving Agents via Harness-Native Training Loop15 October 2026
- Correction Training for Diffusion Models15 October 2026
- Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress15 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.