Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
📰 ArXiv cs.AI
Multi-agent RAG with evolving orchestration and agent prompts improves performance on diverse, multi-hop tasks
Action Steps
- Identify the limitations of static agent behaviors and fixed orchestration strategies in multi-agent RAG
- Develop evolving orchestration mechanisms that adapt to changing task requirements
- Introduce agent prompts that enable agents to learn from experience and improve their performance over time
- Evaluate the effectiveness of the proposed approach on diverse, multi-hop tasks
Who Needs to Know This
AI researchers and engineers working on multi-agent systems and natural language processing can benefit from this approach to improve the robustness and adaptability of their models
Key Insight
💡 Continuously adaptive orchestration mechanisms and agent prompts can improve the robustness and adaptability of multi-agent RAG models
Share This
🤖 Evolving orchestration & agent prompts boost multi-agent RAG performance! 🚀
Key Takeaways
Multi-agent RAG with evolving orchestration and agent prompts improves performance on diverse, multi-hop tasks
Full Article
Title: Experience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts
Abstract:
arXiv:2604.00901v1 Announce Type: new Abstract: Multi-agent Retrieval-Augmented Generation (RAG), wherein each agent takes on a specific role, supports hard queries that require multiple steps and sources, or complex reasoning. Existing approaches, however, rely on static agent behaviors and fixed orchestration strategies, leading to brittle performance on diverse, multi-hop tasks. We identify two key limitations: the lack of continuously adaptive orchestration mechanisms and the absence of beha
Abstract:
arXiv:2604.00901v1 Announce Type: new Abstract: Multi-agent Retrieval-Augmented Generation (RAG), wherein each agent takes on a specific role, supports hard queries that require multiple steps and sources, or complex reasoning. Existing approaches, however, rely on static agent behaviors and fixed orchestration strategies, leading to brittle performance on diverse, multi-hop tasks. We identify two key limitations: the lack of continuously adaptive orchestration mechanisms and the absence of beha
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