Abstract
We quantify the impact of Generative Artificial Intelligence (GenAI) on firm productivity through a series of large-scale randomized field experiments involving millions of users and products at a leading cross-border online retail platform. Over six months in 2023-2024, GenAI-based enhancements were integrated into seven consumer-facing business workflows. We find that GenAI adoption significantly increases sales, with treatment effects ranging from 0\% to 16.3\%, depending on GenAI's marginal contribution relative to existing firm practices. Because inputs and prices were held constant across experimental arms, these gains map directly into total factor productivity improvements. Across the four GenAI applications with positive effects, the implied annual incremental value is approximately \$5 per consumer-an economically meaningful impact given the retailer's scale and the early stage of GenAI adoption. The primary mechanism operates through higher conversion rates, consistent with GenAI reducing frictions in the marketplace and improving consumer experience. We also document substantial heterogeneity: smaller and newer sellers, as well as less experienced consumers, exhibit disproportionately larger gains. Our findings provide novel, large-scale causal evidence on the productivity effects of GenAI in online retail, highlighting both its immediate value and broader potential.

AI Insights

• Marketing tactics boosted sales, yet their potency shifts across product categories, with niche items often reaping the most.
• High‑concentration markets amplify marketing gains, while low‑concentration segments see muted effects.
• Premium‑priced goods tend to respond more strongly to promotional pushes than budget alternatives.
• Tail products—those with modest annual sales—experience disproportionately larger conversion lifts than high‑volume head items.
• Surprisingly, the Google Advertising Titles experiment delivered no measurable uplift, hinting at diminishing returns for certain ad formats.
• The study’s short‑term focus leaves open questions about the durability of these marketing‑induced sales spikes.
• For deeper insight, consult Kotler & Keller’s “Marketing Management” and Cialdini’s “Influence” to unpack the psychological drivers behind these findings.

Abstract
Governance of data, compliance, and business privacy matters, particularly for healthcare and finance businesses. Since the recent emergence of AI enterprise AI assistants enhancing business productivity, safeguarding private data and compliance is now a priority. With the implementation of AI assistants across the enterprise, the zero data retention can be achieved by implementing zero data retention policies by Large Language Model businesses like Open AI and Anthropic and Meta. In this work, we explore zero data retention policies for the Enterprise apps of large language models (LLMs). Our key contribution is defining the architectural, compliance, and usability trade-offs of such systems in parallel. In this research work, we examine the development of commercial AI assistants with two industry leaders and market titans in this arena - Salesforce and Microsoft. Both of these companies used distinct technical architecture to support zero data retention policies. Salesforce AgentForce and Microsoft Copilot are among the leading AI assistants providing much-needed push to business productivity in customer care. The purpose of this paper is to analyze the technical architecture and deployment of zero data retention policy by consuming applications as well as big language models service providers like Open Ai, Anthropic, and Meta.

AI Insights

• Salesforce AgentForce and Microsoft Copilot use distinct micro‑service pipelines to enforce zero data retention, highlighting divergent architectural trade‑offs.
• Cross‑border flows to China can breach GDPR’s data minimization, underscoring the need for region‑specific retention policies.
• Configuration complexity across OpenAI, Anthropic, and Google shows no unified, customer‑agnostic zero‑retention framework.
• Zero retention enforcement adds measurable usability penalties, impacting latency and fine‑tuning flexibility.
• The paper proposes a layered compliance model balancing GDPR, HIPAA, and SOC 2 while preserving productivity, and recommends “A Survey on LLM Security and Privacy” and “On Protecting LLM Data Privacy” for deeper insights.
• Readers will enjoy the comparative analysis of Salesforce’s agent‑centric versus Microsoft’s Copilot‑centric architectures.

Abstract
This paper considers behavior-based price discrimination in the repeated sale of a non-durable good to a single long-lived buyer, by a seller without commitment power. We assume that there is a mixed population of forward-looking ``sophisticated'' buyers and myopic ``naive'' buyers. We investigate the impact of these dynamics on the seller's ability to learn about the buyer and exploit this learning for revenue. We obtain conclusions that differ dramatically with the time horizon of the interactions. To understand short time horizons, we analyze a two-period model, and find that the strategic demand reduction observed with fully sophisticated buyers is robust to the introduction of naive types. In fact, despite the inability of naive buyers to game the pricing algorithm, their introduction can further harm the seller's revenue, due to more intense demand reduction overall. For long horizons, we consider an infinite-horizon model with time discounting. We find that the extreme demand reduction predicted by previous work does not survive the introduction of naive buyers. Instead, we observe equilibria where the seller learns meaningfully despite the sophisticated buyers' demand reduction. We prove that for a natural family of such equilibria, the seller's revenue is not just high, but approximates the revenue attainable with commitment power, even when the fraction of naive types is vanishingly small.

AI Insights

• A tiny share of naive buyers can trigger price exploration, letting the seller learn buyer types without commitment.
• The equilibrium remains a Perfect Bayesian Equilibrium even with only sophisticated buyers, thanks to naive buyers’ probing.
• Simulations show revenue can approach the commitment benchmark across many discount factors.
• Naive buyers curb extreme demand reduction seen in fully sophisticated markets, restoring profitable pricing.
• Extending the model to non‑uniform valuations would address its uniformity assumption.
• Key theory texts: Fudenberg & Tirole’s “Repeated Games and Reputations” and Osborne & Rubinstein’s “A Course in Game Theory.”
• The findings invite exploring richer dynamics, such as multi‑product or stochastic demand settings.

Abstract
Algorithmic decision making is increasingly prevalent, but often vulnerable to strategic manipulation by agents seeking a favorable outcome. Prior research has shown that classifier abstention (allowing a classifier to decline making a decision due to insufficient confidence) can significantly increase classifier accuracy. This paper studies abstention within a strategic classification context, exploring how its introduction impacts strategic agents' responses and how principals should optimally leverage it. We model this interaction as a Stackelberg game where a principal, acting as the classifier, first announces its decision policy, and then strategic agents, acting as followers, manipulate their features to receive a desired outcome. Here, we focus on binary classifiers where agents manipulate observable features rather than their true features, and show that optimal abstention ensures that the principal's utility (or loss) is no worse than in a non-abstention setting, even in the presence of strategic agents. We also show that beyond improving accuracy, abstention can also serve as a deterrent to manipulation, making it costlier for agents, especially those less qualified, to manipulate to achieve a positive outcome when manipulation costs are significant enough to affect agent behavior. These results highlight abstention as a valuable tool for reducing the negative effects of strategic behavior in algorithmic decision making systems.

Abstract
Understanding model decisions is crucial in medical imaging, where interpretability directly impacts clinical trust and adoption. Vision Transformers (ViTs) have demonstrated state-of-the-art performance in diagnostic imaging; however, their complex attention mechanisms pose challenges to explainability. This study evaluates the explainability of different Vision Transformer architectures and pre-training strategies - ViT, DeiT, DINO, and Swin Transformer - using Gradient Attention Rollout and Grad-CAM. We conduct both quantitative and qualitative analyses on two medical imaging tasks: peripheral blood cell classification and breast ultrasound image classification. Our findings indicate that DINO combined with Grad-CAM offers the most faithful and localized explanations across datasets. Grad-CAM consistently produces class-discriminative and spatially precise heatmaps, while Gradient Attention Rollout yields more scattered activations. Even in misclassification cases, DINO with Grad-CAM highlights clinically relevant morphological features that appear to have misled the model. By improving model transparency, this research supports the reliable and explainable integration of ViTs into critical medical diagnostic workflows.

AI Insights

• A reproducible framework ranks ViT explainability across tasks, moving beyond simple accuracy.
• DINO + Grad‑CAM yields sharp, class‑discriminative heatmaps, even on misclassifications, highlighting key morphology.
• Gradient Attention Rollout produces diffuse activations, less useful for clinical interpretation.
• Evaluation relies on accuracy and F1‑score, missing richer interpretability metrics.
• Only four ViT variants and two methods were tested; a broader survey could reveal more explainable models.
• Future work should develop ViT‑specific explainability tools that align with clinical reasoning.
• Suggested reading: “Attention is All You Need” for transformer theory and “Explainable Deep Learning for Medical Imaging” for domain insights.

Abstract
In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant should be capable of anticipating user needs and taking actions proactively when appropriate. To realize this vision, we propose Alpha-Service, a unified framework that addresses two fundamental challenges: Know When to intervene by detecting service opportunities from egocentric video streams, and Know How to provide both generalized and personalized services. Inspired by the von Neumann computer architecture and based on AI glasses, Alpha-Service consists of five key components: an Input Unit for perception, a Central Processing Unit for task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit for long-term personalization, and an Output Unit for natural human interaction. As an initial exploration, we implement Alpha-Service through a multi-agent system deployed on AI glasses. Case studies, including a real-time Blackjack advisor, a museum tour guide, and a shopping fit assistant, demonstrate its ability to seamlessly perceive the environment, infer user intent, and provide timely and useful assistance without explicit prompts.