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

• URL: http://arxiv.org/abs/2505.20670v2
• Date: Thu, 05 Jun 2025 09:49:45 GMT
• Title: MIRROR: Multi-agent Intra- and Inter-Reflection for Optimized Reasoning in Tool Learning
• Authors: Zikang Guo, Benfeng Xu, Xiaorui Wang, Zhendong Mao,
• Abstract summary: Complex tasks involving tool integration pose significant challenges for Large Language Models.<n> Reflection has emerged as an effective strategy for correcting erroneous trajectories in agentic benchmarks.<n>We propose MIRROR, a framework that consists of both intra-reflection, which critically assesses intended actions before execution, and inter-reflection, which further adjusts the trajectory.
• Score: 33.009759731505746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. In this paper, we propose MIRROR, a framework that consists of both intra-reflection, which critically assesses intended actions before execution, and inter-reflection, which further adjusts the trajectory based on observations. This design systematically leverages LLM reflection capabilities to eliminate and rectify erroneous actions on a more comprehensive scope. Evaluations on both the StableToolBench and TravelPlanner benchmarks demonstrate MIRROR's superior performance, achieving state-of-the-art results compared to existing approaches.

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