Abstract
Quick commerce (q-commerce) is one of the fastest growing sectors in India. It provides informal employment to approximately 4,50,000 workers, and it is estimated to become a USD 200 Billion industry by 2026. A significant portion of this industry deals with perishable goods. (e.g. milk, dosa batter etc.) These are food items which are consumed relatively fresh by the consumers and therefore their order volume is high and repetitive even when the average basket size is relatively small. The fundamental challenge for the retailer is that, increasing selling price would hamper sales and would lead to unsold inventory. On the other hand setting a price less, would lead to forgoing of potential revenue. This paper attempts to propose a mathematical model which formalizes this dilemma. The problem statement is not only important for improving the unit economics of the perennially loss making quick commerce firms, but also would lead to a trickle-down effect in improving the conditions of the gig workers as observed in [4]. The sections below describe the mathematical formulation. The results from the simulation would be published in a follow-up study.

Abstract
Dynamic pricing is the practice of adjusting the selling price of a product to maximize a firm's revenue by responding to market demand. The literature typically distinguishes between two settings: infinite inventory, where the firm has unlimited stock and time to sell, and finite inventory, where both inventory and selling horizon are limited. In both cases, the central challenge lies in the fact that the demand function -- how sales respond to price -- is unknown and must be learned from data. Traditional approaches often assume a specific parametric form for the demand function, enabling the use of reinforcement learning (RL) to identify near-optimal pricing strategies. However, such assumptions may not hold in real-world scenarios, limiting the applicability of these methods. In this work, we propose a Gaussian Process (GP) based nonparametric approach to dynamic pricing that avoids restrictive modeling assumptions. We treat the demand function as a black-box function of the price and develop pricing algorithms based on Bayesian Optimization (BO) -- a sample-efficient method for optimizing unknown functions. We present BO-based algorithms tailored for both infinite and finite inventory settings and provide regret guarantees for both regimes, thereby quantifying the learning efficiency of our methods. Through extensive experiments, we demonstrate that our BO-based methods outperform several state-of-the-art RL algorithms in terms of revenue, while requiring fewer assumptions and offering greater robustness. This highlights Bayesian Optimization as a powerful and practical tool for dynamic pricing in complex, uncertain environments.

AI Insights

• GP‑Fin‑Model‑Based runs in O(n³+CT·P) time, while BO‑Fin‑Heuristic scales as O(n³+TP), exposing a linear‑vs‑superlinear gap.
• Experiments confirm the heuristic matches theoretical bounds and is 3× faster than the GP model for large C or T.
• In real‑time, resource‑constrained settings, the BO‑Fin‑Heuristic’s linear growth makes it the practical choice; the GP model stalls.
• The study casts dynamic pricing as a finite‑horizon MDP with Bernoulli demand, a nuance omitted from the abstract.
• Core terms: GP‑Fin‑Model‑Based = GP‑based demand estimation; BO‑Fin‑Heuristic = Bayesian‑optimization‑driven heuristic.
• Suggested reading: Devroye et al. (2011), Kiefer & Wolfowitz (1956), Vapnik (1998) for foundational theory.
• One might ask: can a hybrid leverage the GP’s accuracy while retaining the heuristic’s speed?

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
With the rapid development of artificial intelligence (AI) technology, socio-economic systems are entering a new stage of "human-AI co-creation." Building upon a previously established multi-level intelligent agent economic model, this paper conducts simulation-based comparisons of macroeconomic output evolution in China and the United States under different mechanisms-AI collaboration, network effects, and AI autonomous production. The results show that: (1) when AI functions as an independent productive entity, the overall growth rate of social output far exceeds that of traditional human-labor-based models; (2) China demonstrates clear potential for acceleration in both the expansion of intelligent agent populations and the pace of technological catch-up, offering the possibility of achieving technological convergence or even partial surpassing. This study provides a systematic, model-based analytical framework for understanding AI-driven production system transformation and shifts in international competitiveness, as well as quantitative insights for relevant policy formulation.

AI Insights

• China’s AI market hit $578 billion in 2023, overtaking the U.S. in scale.
• Global AI server demand is projected to reach $187 billion in 2024, 65 % of the server market.
• Generative‑AI spending is forecast to hit $1.5 trillion by 2030, reshaping developer productivity.
• Network effects drive AI adoption, delivering increasing returns as platform users grow.
• The simulation framework integrates Stanford AI Index, Penn World Table, and McKinsey data for robust macro‑economic forecasting.
• Literature such as “Virtual Agent Economies” and “Internet Diffusion Research” informs the agent‑based model’s diffusion dynamics.
• While the study focuses on economic gains, it acknowledges gaps in social and environmental impact analysis.

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
As people become more conscious of their health and the environment, the demand for organic food is expected to increase. However, distinguishing organic products from conventionally produced ones can be hard, creating a problem where producers may have the incentive to label their conventional products as organic to sell them at a higher price. Game theory can help to analyze the strategic interactions between producers and consumers in order to help consumers verifying these claims. Through a game theory analysis approach, this paper provides evidence of the need for a third party to equalize markets and foster trust in organic supply chains. Therefore, government regulation, including regular and random monitoring and certification requirements, plays a crucial role in achieving the desired level of trust and information exchange among supply chain agents, which ultimately determines the growth trajectory of the sector.

Abstract
Supply chain attacks significantly threaten software security with malicious code injections within legitimate projects. Such attacks are very rare but may have a devastating impact. Detecting spurious code injections using automated tools is further complicated as it often requires deciphering the intention of both the inserted code and its context. In this study, we propose an unsupervised approach for highlighting spurious code injections by quantifying cohesion disruptions in the source code. Using a name-prediction-based cohesion (NPC) metric, we analyze how function cohesion changes when malicious code is introduced compared to natural cohesion fluctuations. An analysis of 54,707 functions over 369 open-source C++ repositories reveals that code injection reduces cohesion and shifts naming patterns toward shorter, less descriptive names compared to genuine function updates. Considering the sporadic nature of real supply-chain attacks, we evaluate the proposed method with extreme test-set imbalance and show that monitoring high-cohesion functions with NPC can effectively detect functions with injected code, achieving a Precision@100 of 36.41% at a 1:1,000 ratio and 12.47% at 1:10,000. These results suggest that automated cohesion measurements, in general, and name-prediction-based cohesion, in particular, may help identify supply chain attacks, improving source code integrity.

Abstract
Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. Applying the framework to a Massachusetts case study, we find that market participation could deliver similar savings as peak demand reduction. Under current conditions, market participation does not increase storage investment, but at very low storage costs, could incentivize deployment beyond local distribution needs. This might run contrary to the separation of distribution from generation in deregulated markets. Our framework can identify investment levels appropriate for local distribution needs.