MindFlow+: A Self-Evolving Agent for E-Commerce Customer Service

• URL: http://arxiv.org/abs/2507.18884v1
• Date: Fri, 25 Jul 2025 02:01:55 GMT
• Title: MindFlow+: A Self-Evolving Agent for E-Commerce Customer Service
• Authors: Ming Gong, Xucheng Huang, Ziheng Xu, Vijayan K. Asari,
• Abstract summary: Self-evolving dialogue agent MindFlow+ learns domain-specific behavior by combining large language models with imitation learning and offline reinforcement learning.<n>MindFlow+ introduces two data-centric mechanisms to guide learning: tool-augmented demonstration construction, and reward-conditioned data modeling.<n>Experiments on real-world e-commerce conversations show that MindFlow+ outperforms strong baselines in contextual relevance, flexibility, and task accuracy.
• Score: 22.012089343697767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality dialogue is crucial for e-commerce customer service, yet traditional intent-based systems struggle with dynamic, multi-turn interactions. We present MindFlow+, a self-evolving dialogue agent that learns domain-specific behavior by combining large language models (LLMs) with imitation learning and offline reinforcement learning (RL). MindFlow+ introduces two data-centric mechanisms to guide learning: tool-augmented demonstration construction, which exposes the model to knowledge-enhanced and agentic (ReAct-style) interactions for effective tool use; and reward-conditioned data modeling, which aligns responses with task-specific goals using reward signals. To evaluate the model's role in response generation, we introduce the AI Contribution Ratio, a novel metric quantifying AI involvement in dialogue. Experiments on real-world e-commerce conversations show that MindFlow+ outperforms strong baselines in contextual relevance, flexibility, and task accuracy. These results demonstrate the potential of combining LLMs tool reasoning, and reward-guided learning to build domain-specialized, context-aware dialogue systems.

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