arXiv:2508.04266v4 Announce Type: replace Abstract: Existing benchmarks in e-commerce primarily focus on basic user intents, such as finding or purchasing products. However, real-world users often pursue more complex goals, such as applying vouchers, managing budgets, and finding multi-products seller. To bridge this gap, we propose ShoppingBench, a novel end-to-end shopping benchmark designed to encompass increasingly challenging levels of grounded intent. Specifically, we propose a scalable framework to simulate user instructions based on various intents derived from sampled real-world produ
The rapid advancement and adoption of LLMs are pushing the need for more sophisticated and 'real-world' benchmarks to evaluate their agentic capabilities in complex tasks.
This benchmark signifies a crucial step toward developing more capable and robust AI agents for complex real-world interaction, moving beyond simple transactions to nuanced user intents.
The evaluation of AI agents will shift from basic task completion to assessing their ability to handle multi-step, intent-grounded e-commerce scenarios, accelerating their practical utility.
- · AI agent developers
- · E-commerce platforms leveraging AI
- · Consumers through improved AI assistants
- · Simple rule-based automation systems
- · Firms slow to adopt advanced AI agents
AI agents will become significantly more adept at handling complex user requests in e-commerce and other sectors.
This improved capability could lead to a rapid expansion of AI agents into white-collar roles requiring complex decision-making and interaction.
The development of highly sophisticated, intent-grounded agents could fundamentally alter how consumers interact with digital platforms and conduct online tasks, potentially collapsing existing SaaS layers.
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