ProductResearch: 通过多智能体合成轨迹蒸馏训练电商深度研究代理
ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation
AI总结：
ProductResearch通过多代理合成轨迹蒸馏训练电商深度研究代理，提升复杂购物查询的响应全面性和用户效用。
摘要：
基于大型语言模型（LLM）的代理在电商对话购物中展现出潜力，但现有实现缺乏复杂产品研究所需的交互深度和上下文广度。同时，尽管深度研究范式在网页搜索信息合成方面有所进展，但在转移到电商领域时仍存在领域差距。我们提出ProductResearch，一个多代理框架，通过合成高保真、长周期工具使用轨迹来训练鲁棒的电商购物代理。该框架采用用户代理从行为历史中推断出细微的购物意图，以及一个监督代理，通过与研究代理的迭代协作生成合成轨迹，最终生成全面且有洞察力的产品研究报告。这些轨迹通过严格的反思内化过程进行过滤和蒸馏，将多代理监督交互整合为连贯的单角色训练示例，从而有效微调LLM代理以处理复杂的购物查询。大量实验表明，一个在我们合成数据上微调的紧凑MoE模型在响应全面性、研究深度和用户感知效用方面显著优于其基础模型，接近前沿专有深度研究系统的性能，并确立了多代理合成轨迹训练作为增强LLM购物辅助的有效且可扩展范式。
英文摘要：
Large Language Model (LLM)-based agents show promise for e-commerce conversational shopping, yet existing implementations lack the interaction depth and contextual breadth required for complex product research. Meanwhile, the Deep Research paradigm, despite advancing information synthesis in web search, suffers from domain gaps when transferred to e-commerce. We propose ProductResearch, a multi-agent framework that synthesizes high-fidelity, long-horizon tool-use trajectories for training robust e-commerce shopping agents. The framework employs a User Agent to infer nuanced shopping intents from behavioral histories, and a Supervisor Agent that orchestrates iterative collaboration with a Research Agent to generate synthetic trajectories culminating in comprehensive, insightful product research reports. These trajectories are rigorously filtered and distilled through a reflective internalization process that consolidates multi-agent supervisory interactions into coherent single-role training examples, enabling effective fine-tuning of LLM agents for complex shopping inquiries. Extensive experiments show that a compact MoE model fine-tuned on our synthetic data achieves substantial improvements over its base model in response comprehensiveness, research depth, and user-perceived utility, approaching the performance of frontier proprietary deep research systems and establishing multi-agent synthetic trajectory training as an effective and scalable paradigm for enhancing LLM-based shopping assistance.
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