arXiv (All)
open access
Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning
Large language model (LLM) agents are increasingly deployed in multi-agent systems (MAS) to accomplish complex tasks. Prior black-box red-teaming attacks mainly focus on single agents, but we find that these methods are far less effective in MAS, where multiple sub-agents may not directly interact w
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Black-Box Red-Teaming of Multi-Agent Systems via Reinforcement Learning#1406399
- OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution#1131626
- Bias Control and Injection for Multi-Agent Aggregation#1356626
- Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models#1413516
- Consensus and Factual Dynamics in Large Populations of Interacting Language Models#1144178
Record · ID 1413207
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive