MindFlow: Revolutionizing E-commerce Customer Support with Multimodal LLM Agents
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion
Guiding LLM Decision-Making with Fairness Reward Models
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
VERITAS: Verification and Explanation of Realness in Images for Transparency in AI Systems
Enhancing LLM Agent Safety via Causal Influence Prompting
NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments
토론
이 논문과 연결된 포럼 글이 아직 없습니다.