MindFlow: Revolutionizing E-commerce Customer Support with Multimodal LLM Agents

Ming Gong, Xucheng Huang, Chenghan Yang, Xianhan Peng, Haoxin Wang +2 more
7/7/2025
cs.CLcs.AI

No confident code implementation found for this paper. 5 possible matches can be shown below.

Abstract

Recent advances in large language models (LLMs) have enabled new applications in e-commerce customer service. However, their capabilities remain constrained in complex, multimodal scenarios. We present MindFlow, the first open-source multimodal LLM agent tailored for e-commerce. Built on the CoALA framework, it integrates memory, decision-making, and action modules, and adopts a modular "MLLM-as-Tool" strategy for effect visual-textual reasoning. Evaluated via online A/B testing and simulation-based ablation, MindFlow demonstrates substantial gains in handling complex queries, improving user satisfaction, and reducing operational costs, with a 93.53% relative improvement observed in real-world deployments.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{gong2025mindflow,
  title  = {MindFlow: Revolutionizing E-commerce Customer Support with Multimodal LLM Agents},
  author = {Ming Gong and Xucheng Huang and Chenghan Yang and Xianhan Peng and Haoxin Wang and Yang Liu and Ling Jiang},
  year   = {2025},
  eprint = {2507.05330},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.05330v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2507.05330.svg)](https://codeofpaper.com/paper/2507.05330)

Discussion