Dynamic Retrieval Scheduling in Multi-Agent Debate Frameworks versus Fixed-Context Augmentation for FEVER-LC Verification Accuracy

Large Language Models (LLMs) suffer from hallucinations and factual inaccuracies, especially in complex reasoning and fact verification tasks. Multi-Agent Debate (MAD) systems aim to improve answer accuracy by enabling multiple LLM agents to engage in dialogue, promoting diverse reasoning and mutual verification. However, existing MAD frameworks primarily rely on internal knowledge or static documents, making them vulnerable to hallucinations. While MADKE introduces external evidence to mitigate this, its one-time retrieval mechanism limits adaptability to new arguments or emerging information

Research goal: How does dynamic retrieval scheduling in multi-agent debate frameworks impact FEVER-LC verification accuracy compared to fixed-context augmentation strategies?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.1/10.