Council Mode: Mitigating Hallucination and Bias in LLMs via Multi-Agent Consensus

ArXi:2604.02923v1 Announce Type: cross Large Language Models (LLMs), particularly those employing Mixture-of-Experts (MoE) architectures, have achieved remarkable capabilities across diverse natural language processing tasks. However, these models frequently suffer from hallucinations -- generating plausible but factually incorrect content -- and exhibit systematic biases that are amplified by uneven expert activation during inference.