arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queries and dispatches them to parallel sub-agents. However, existing systems instantiate all roles from a single model of identical scale, leaving open how model capacity should be distributed across roles. We factorize hierarchical search into three roles: a delegation role responsible for task decomposition, an execution role responsible for retrieval and evidence extraction, and an answer generatio
The proliferation of Large Language Models (LLMs) and multi-agent architectures necessitates research into optimal resource allocation and system design for practical and efficient AI systems.
This research provides a framework for understanding and optimizing the internal architecture of multi-agent LLM systems, which is crucial for advancing their capabilities and efficiency in complex tasks.
The understanding of how to distribute model capacity across different roles in hierarchical AI agents will inform future design choices, leading to more performant and resource-efficient search and analysis systems.
- · AI researchers
- · LLM developers
- · Cloud providers
- · Enterprises leveraging AI agents
- · Inefficient monolithic AI agent designs
- · Companies with undifferentiated LLM offerings
More sophisticated and efficient multi-agent LLM systems will emerge for complex problem-solving.
Optimized multi-agent architectures could significantly reduce computational costs and scale the deployment of AI agents across various industries.
The ability to finely tune agent roles and capacities could lead to the development of highly specialized and powerful 'AI teams' that mimic human organizational structures.
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Read at arXiv cs.CL