Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
📰 ArXiv cs.AI
Learn to build an iterative critique-and-routing controller for multi-agent systems with heterogeneous LLMs to improve output quality through iterative refinement
Action Steps
- Design a critique-and-routing controller architecture using heterogeneous LLMs
- Implement iterative refinement mechanisms to critique intermediate drafts
- Train the controller using reinforcement learning or other optimization methods
- Test the controller with various multi-agent systems and LLM configurations
- Evaluate the performance of the controller using metrics such as output quality and refinement efficiency
Who Needs to Know This
AI engineers and researchers working on multi-agent systems can benefit from this approach to improve the coordination and output of heterogeneous LLMs
Key Insight
💡 Iterative refinement through critique-and-routing controllers can significantly improve the output quality of multi-agent systems with heterogeneous LLMs
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🤖 Improve multi-agent LLM systems with iterative critique-and-routing controllers! 🚀
Key Takeaways
Learn to build an iterative critique-and-routing controller for multi-agent systems with heterogeneous LLMs to improve output quality through iterative refinement
Full Article
Title: Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
Abstract:
arXiv:2605.08686v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typically limited to one-shot routing: they select a model once and return its output directly. Such routing-only designs provide no mechanism to critique intermediate drafts or support iterative refinement. To address this limitation, we propose a critique-and-routing controller that casts multi-agent
Abstract:
arXiv:2605.08686v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typically limited to one-shot routing: they select a model once and return its output directly. Such routing-only designs provide no mechanism to critique intermediate drafts or support iterative refinement. To address this limitation, we propose a critique-and-routing controller that casts multi-agent
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