AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering

ArXi:2605.17352v1 Announce Type: new Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucinations and the limitations of LLMs in bridging long-tail knowledge gaps. To address this, we propose AMATA, an Adaptive Multi-Agent Trajectory Alignment framework that dynamically integrates external knowledge to improve response interpretability and factual grounding.