arXiv (All)
open access
MAS-on-the-Fly: In-Context Structural Adaptation of LLM-Based Multi-Agent Systems
Large Language Model (LLM)-based multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, existing works often rely on manual designs or "one-size-fits-all" automation and lack adaptability after deployment. We study in-context structural adaptation, where structure
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
- MAS-on-the-Fly: In-Context Structural Adaptation of LLM-Based Multi-Agent Systems#797361
- UniACE: A Unified Framework for Evaluating LLM Agentic Capabilities#820473
- NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs#381815
- Concealing LLM-Based Multi-Agent Topology via Phantom Structure Injection#1141886
- Concealing LLM-Based Multi-Agent Topology via Phantom Structure Injection#1122194
Record · ID 798775
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