  Recent advancements in Large Language Models (LLMs) have led to significant
breakthroughs in various natural language processing tasks. However, generating
factually consistent responses in knowledge-intensive scenarios remains a
challenge due to issues such as hallucination, difficulty in acquiring
long-tailed knowledge, and limited memory expansion. This paper introduces
SMART, a novel multi-agent framework that leverages external knowledge to
enhance the interpretability and factual consistency of LLM-generated
responses. SMART comprises four specialized agents, each performing a specific
sub-trajectory action to navigate complex knowledge-intensive tasks. We propose
a multi-agent co-training paradigm, Long- and Short-Trajectory Learning, which
ensures synergistic collaboration among agents while maintaining fine-grained
execution by each agent. Extensive experiments on 5 tasks demonstrate SMART's
superior performance compared to previous widely adopted methods.
