Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias
arXiv:2604.02923v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases. These issues, sometimes amplified in specific architectures like Mixture-of-Experts (MoE) which motivate our work, pose risks for reliable deployment. To address these challenges, we propose the Council Mode, a multi-agent consensus framework. Our approach dispatches queries to multiple heterogeneous frontier LLMs in parallel and synthesizes their outputs using a dedicated consensus mode
The accelerating deployment and increasing capabilities of large language models necessitate robust solutions for their inherent limitations like hallucination and bias, especially as they move towards more critical applications.
Reducing LLM hallucination and bias is critical for the reliable and trustworthy deployment of AI across all sectors, impacting everything from enterprise automation to scientific discovery.
The development of multi-agent consensus frameworks offers a significant architectural approach to improve the accuracy and reliability of LLM outputs, moving beyond single-model limitations.
- · AI developers
- · Enterprises adopting AI
- · AI Ethics and Safety researchers
- · Users of AI systems
- · Providers of unreliable AI services
- · Single-model AI architectures for critical tasks
Improved reliability of AI outputs will accelerate the adoption of LLMs in regulated and sensitive industries.
The demand for heterogeneous LLMs and robust consensus mechanisms will drive further research into diverse model architectures and more sophisticated agentic coordination.
Enhanced trust in AI systems could lead to a re-evaluation of regulatory frameworks, potentially accelerating AI integration into societal infrastructure with fewer restrictions.
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Read at arXiv cs.AI