arXiv:2603. 29888v2 Announce Type: replace-cross Abstract: In collaboration with Alibaba, we study how a generative AI assistant affects service performance in e-commerce after-sales operations.
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arXiv:2510. 12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform.
The paper introduces a hypothesis-driven simulation workflow that screens customer experience (CX) agents before deployment, using synthetic customers and simulated tool outputs to emulate multi-step interactions without accessing production backends. Applied to Nubank’s high-volume Card Delivery and Card Management chat-support agents, the simulation’s binary evaluator scores correlated strongly with production results, and simulation-guided iterations raised transactional net promoter score by 36.69 points in a live A/B test. Additionally, screening over 16,000 simulated conversations helped select a model that increased self‑service rate by 8.82 percentage points without harming net promoter score, demonstrating that simulation enables extensive model exploration safely.
arXiv:2606. 12924v1 Announce Type: new Abstract: We present a modular two-agent simulation framework for evaluating conversational shopping assistant architectures.
arXiv:2608. 04205v1 Announce Type: new Abstract: Human evaluation of AI systems and digital products is costly, slow, and difficult to scale.
The paper describes a production migration of a large-scale customer‑support conversational assistant from a single blended model to a Dynamic Response (DR) architecture. DR replaces the Qwen3‑235B‑A22B responder with a bounded ReAct orchestrator that selects typed tools and a smaller generator that writes from a validated context contract. The migration yields significant improvements: precision‑first entity selection boosts reservation selector precision from 8.3% to 89.1%, typed action IDs eliminate structured‑action hallucination, and hard‑escalation responses drop from 5.60% to 3.08%. Latency is reduced from 3.87 s to 2.24 s, GPU usage is cut by roughly one‑third, and self‑hosting cuts annual model‑serving costs by more than an order of magnitude.
arXiv:2605.14542v2 Announce Type: replace Abstract: A skilled live-commerce host is not merely a narrator, but a sales agent who converts viewer curiosity into purchase intent through expert product...