arXiv (All)
open access
Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent Systems
Adaptive multi-agent systems (MAS) are increasingly adopted to tackle complex problems. However, the narrow task coverage of their optimization raises the question of whether they can function as general-purpose systems. To address this gap, we conduct an extensive empirical study of adaptive MAS, r
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
- MACE: Memory-Agent Co-Evolution with Adaptive Memory Graphs for Multi-Agent Systems#1035931
- MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination#1248453
- ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search#927996
- Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization#1012234
- Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use#329124
Record · ID 1399841
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