A new arXiv paper studies hallucination reduction in mixture-of-experts language models, a model design that routes tokens through selected expert subnetworks rather than using the same full network for every token.
The authors focus on contrastive decoding, a technique that compares signals from different model layers during generation. Prior work has largely examined standard transformer models, while this paper asks whether similar ideas apply when expert routing changes the internal computation.
The proposed expert-aware approach is meant to use information from the model’s own routing and layer behavior to reduce unsupported outputs. That is relevant because hallucinations remain one of the main barriers to using LLMs in search, support, analysis, and agent systems.
The work is an empirical research study, not a guarantee that MoE systems can be made factual by decoding alone. Its value is in narrowing the question: if expert routing changes where knowledge appears inside a model, hallucination-mitigation methods may need to account for that architecture directly.