arXiv:2603.14864v2 Announce Type: replace Abstract: In e-commerce, LLM agents show promise for shopping tasks such as recommendations, budget management, and bundle deals, where accurately capturing user preferences from long-horizon conversations is critical. However, progress is limited by two key challenges: (1) the absence of benchmarks for evaluating long-term preference-aware shopping tasks, and (2) the lack of fine-grained supervision for shopping agent training. To fill the benchmark gap, we introduce Shopping Companion Bench, a novel benchmark comprising two shopping tasks that requir
The proliferation of Large Language Models (LLMs) and the increasing demand for personalized online shopping experiences are driving the need for more sophisticated AI agents in e-commerce.
This work directly addresses a critical gap in the development and evaluation of memory-augmented LLM agents for long-term, preference-aware e-commerce tasks, potentially unlocking significant automation in retail.
The introduction of a new benchmark and the framework for 'Shopping Companion' provide a standardized way to measure and train AI agents capable of understanding and acting on complex user preferences over time.
- · E-commerce platforms
- · AI agent developers
- · Online shoppers
- · Retailers adopting AI
- · Manual customer support agents
- · E-commerce platforms slow to adopt AI
More sophisticated and personalized shopping experiences will become standard in e-commerce, driven by memory-augmented LLM agents.
This will lead to increased conversion rates and customer satisfaction for businesses adopting these AI tools, as well as a significant shift in e-commerce labor demands.
The success of these agents could accelerate the development of similar autonomous LLM agents across other service industries, further collapsing white-collar workflows.
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