MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning

Understand

Complex tasks involving tool integration pose significant challenges for Large Language Models (LLMs), leading to the emergence of multi-agent workflows as a promising solution.

• Reflection has emerged as an effective strategy for correcting erroneous trajectories in agentic workflows.
• However, existing approaches only exploit such capability in the post-action stage, where the agent observes the execution outcomes.
• We argue that, like humans, LLMs can also engage in reflection before action execution: the agent can anticipate undesirable outcomes from its own decisions, which not only provides a necessarily complementary perspective to evaluate the decision but also prevents the propagation of errors throughout the trajectory.