Abstract
Bundle pricing refers to designing several product combinations (i.e., bundles) and determining their prices in order to maximize the expected profit. It is a classic problem in revenue management and arises in many industries, such as e-commerce, tourism, and video games. However, the problem is typically intractable due to the exponential number of candidate bundles. In this paper, we explore the usage of graph convolutional networks (GCNs) in solving the bundle pricing problem. Specifically, we first develop a graph representation of the mixed bundling model (where every possible bundle is assigned with a specific price) and then train a GCN to learn the latent patterns of optimal bundles. Based on the trained GCN, we propose two inference strategies to derive high-quality feasible solutions. A local-search technique is further proposed to improve the solution quality. Numerical experiments validate the effectiveness and efficiency of our proposed GCN-based framework. Using a GCN trained on instances with 5 products, our methods consistently achieve near-optimal solutions (better than 97%) with only a fraction of computational time for problems of small to medium size. It also achieves superior solutions for larger size of problems compared with other heuristic methods such as bundle size pricing (BSP). The method can also provide high quality solutions for instances with more than 30 products even for the challenging cases where product utilities are non-additive.

Abstract
Our society can benefit immensely from algorithmic decision-making and similar types of artificial intelligence. But algorithmic decision-making can also have discriminatory effects. This paper examines that problem, using online price differentiation as an example of algorithmic decision-making. With online price differentiation, a company charges different people different prices for identical products, based on information the company has about those people. The main question in this paper is: to what extent can non-discrimination law protect people against online price differentiation? The paper shows that online price differentiation and algorithmic decision-making could lead to indirect discrimination, for instance harming people with a certain ethnicity. Indirect discrimination occurs when a practice is neutral at first glance, but ends up discriminating against people with a protected characteristic, such as ethnicity. In principle, non-discrimination law prohibits indirect discrimination. The paper also shows, however, that non-discrimination law has flaws when applied to algorithmic decision-making. For instance, algorithmic discrimination can remain hidden: people may not realise that they are being discriminated against. And many types of unfair - some might say discriminatory - algorithmic decisions are outside the scope of current non-discrimination law.

Abstract
KT developed a Responsible AI (RAI) assessment methodology and risk mitigation technologies to ensure the safety and reliability of AI services. By analyzing the Basic Act on AI implementation and global AI governance trends, we established a unique approach for regulatory compliance and systematically identify and manage all potential risk factors from AI development to operation. We present a reliable assessment methodology that systematically verifies model safety and robustness based on KT's AI risk taxonomy tailored to the domestic environment. We also provide practical tools for managing and mitigating identified AI risks. With the release of this report, we also release proprietary Guardrail : SafetyGuard that blocks harmful responses from AI models in real-time, supporting the enhancement of safety in the domestic AI development ecosystem. We also believe these research outcomes provide valuable insights for organizations seeking to develop Responsible AI.

AI Insights

• The risk taxonomy categorizes threats into data, model, deployment, and societal dimensions, each with measurable indicators.
• A multi‑stage assessment pipeline integrates static code analysis, adversarial testing, and human‑in‑the‑loop audits to quantify robustness.
• SafetyGuard employs a lightweight transformer‑based policy network that intercepts outputs in real time, achieving <5 ms latency on edge devices.
• Compliance mapping aligns each risk factor with specific clauses of the Basic Act on AI, enabling automated audit reports.
• Pilot deployments in Korean telecom and finance sectors demonstrated a 30 % reduction in policy‑violating incidents after Guardrail integration.
• The report proposes a future research agenda on explainable mitigation strategies and cross‑border data‑sharing protocols.

Abstract
Global food systems must deliver nutritious and sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical, and fragmented. Artificial intelligence (AI) now offers a transformative path with the potential to link molecular composition to functional performance, bridge chemical structure to sensory outcomes, and accelerate cross-disciplinary innovation across the entire production pipeline. Here we outline AI for Food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory properties, manufacturing, and recipe generation. Early successes demonstrate how AI can predict protein performance, map molecules to flavor, and tailor consumer experiences. But significant challenges remain: lack of standardization, scarce multimodal data, cultural and nutritional diversity, and low consumer confidence. We propose three priorities to unlock the field: treating food as a programmable biomaterial, building self-driving laboratories for automated discovery, and developing deep reasoning models that integrate sustainability and human health. By embedding AI responsibly into the food innovation cycle, we can accelerate the transition to sustainable protein systems and chart a predictive, design-driven science of food for our own health and the health of our planet.

Abstract
Modern socio-economic systems are undergoing deep integration with artificial intelligence technologies. This paper constructs a heterogeneous agent-based modeling framework that incorporates both human workers and autonomous AI agents, to study the impact of AI collaboration under resource constraints on aggregate social output. We build five progressively extended models: Model 1 serves as the baseline of pure human collaboration; Model 2 introduces AI as collaborators; Model 3 incorporates network effects among agents; Model 4 treats agents as independent producers; and Model 5 integrates both network effects and independent agent production. Through theoretical derivation and simulation analysis, we find that the introduction of AI agents can significantly increase aggregate social output. When considering network effects among agents, this increase exhibits nonlinear growth far exceeding the simple sum of individual contributions. Under the same resource inputs, treating agents as independent producers provides higher long-term growth potential; introducing network effects further demonstrates strong characteristics of increasing returns to scale.

Abstract
This paper develops a taxonomy of expert perspectives on the risks and likely consequences of artificial intelligence, with particular focus on Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). Drawing from primary sources, we identify three predominant doctrines: (1) The dominance doctrine, which predicts that the first actor to create sufficiently advanced AI will attain overwhelming strategic superiority sufficient to cheaply neutralize its opponents' defenses; (2) The extinction doctrine, which anticipates that humanity will likely lose control of ASI, leading to the extinction of the human species or its permanent disempowerment; (3) The replacement doctrine, which forecasts that AI will automate a large share of tasks currently performed by humans, but will not be so transformative as to fundamentally reshape or bring an end to human civilization. We examine the assumptions and arguments underlying each doctrine, including expectations around the pace of AI progress and the feasibility of maintaining advanced AI under human control. While the boundaries between doctrines are sometimes porous and many experts hedge across them, this taxonomy clarifies the core axes of disagreement over the anticipated scale and nature of the consequences of AI development.

Abstract
This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating the control problem in a general dynamic programming framework, introducing the mathematical structure needed for a detailed convergence analysis. The associate value function is estimated through a sequence of approximations combining nonparametric regression methods and Monte Carlo subsampling. The regression step is performed within reproducing kernel Hilbert spaces (RKHSs), exploiting the classical KRR algorithm, while Monte Carlo sampling methods are introduced to estimate the continuation value. To assess the accuracy of our value function estimator, we propose a natural error decomposition and rigorously control the resulting error terms at each time step. We then analyze how this error propagates backward in time-from maturity to the initial stage-a relatively underexplored aspect of the SOC literature. Finally, we illustrate how our analysis naturally applies to a key financial application: the pricing of American options.

Abstract
In recent years, various software supply chain (SSC) attacks have posed significant risks to the global community. Severe consequences may arise if developers integrate insecure code snippets that are vulnerable to SSC attacks into their products. Particularly, code generation techniques, such as large language models (LLMs), have been widely utilized in the developer community. However, LLMs are known to suffer from inherent issues when generating code, including fabrication, misinformation, and reliance on outdated training data, all of which can result in serious software supply chain threats. In this paper, we investigate the security threats to the SSC that arise from these inherent issues. We examine three categories of threats, including eleven potential SSC-related threats, related to external components in source code, and continuous integration configuration files. We find some threats in LLM-generated code could enable attackers to hijack software and workflows, while some others might cause potential hidden threats that compromise the security of the software over time. To understand these security impacts and severity, we design a tool, SSCGuard, to generate 439,138 prompts based on SSC-related questions collected online, and analyze the responses of four popular LLMs from GPT and Llama. Our results show that all identified SSC-related threats persistently exist. To mitigate these risks, we propose a novel prompt-based defense mechanism, namely Chain-of-Confirmation, to reduce fabrication, and a middleware-based defense that informs users of various SSC threats.

AI Insights

• Eleven SSC‑related threats were catalogued, covering external code components and CI config files.
• 439,138 prompts probed GPT and Llama, revealing all threats persist.
• Prompt injection and jailbreak attacks were validated as vectors against LLM‑integrated apps.
• A middleware layer was proposed to alert developers to emerging SSC risks in real time.
• Chain‑of‑Confirmation prompts curb fabrication, cutting false‑positive snippets.
• Hidden, time‑driven vulnerabilities were found, showing LLM code can silently erode security over releases.
• Even well‑trained LLMs can supply malicious dependencies, underscoring continuous SSC monitoring.

Abstract
The paper proposes a novel Economic Production Quantity (EPQ) inventory model within a reverse logistics framework, addressing new and repaired products with varying quality and demand patterns. The model integrates production and remanufacturing rates as functions of lot sizes and cycle numbers to develop a feasible inventory cost function. A key contribution of the study is formulating a multiobjective optimization framework that simultaneously minimizes inventory costs and accounts for environmental sustainability by considering greenhouse gas (GHG) emissions and energy consumption during production processes. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model, with integer constraints on lot sizes and cycle counts and a continuous return rate. Numerical case studies taking test problems from existing literature are used to validate the model through extensive sensitivity analyses. Both mathematical optimization and heuristic optimization methods are applied to solve multiobjective optimization problems, and Pareto fronts are illustrated along with the interpretation of the results. The results, obtained using solvers in MATLAB and AMPL, highlight the models ability to balance operational efficiency and environmental responsibility. Pareto frontiers generated from the analysis provide strategic insights for decision-makers seeking to optimize cost and sustainability in inventory systems.

AI Insights

• The model assumes perfect knowledge of return rates, a practical limitation not mentioned in the abstract.
• No computational complexity analysis is provided, leaving scalability questions open.
• The authors recommend “Nonlinear Multiobjective Optimization” for deeper mathematical insight.
• A 2021 study on price‑ and quality‑dependent return rates is cited, illustrating market dynamics integration.
• Learning effects are modeled in remanufacturing rates, a nuance absent from the abstract.
• An algorithmic framework for convex mixed‑integer nonlinear programs is proposed as an alternative to heuristics.
• The case study reports a 15 % GHG reduction while keeping costs unchanged, a concrete performance metric.

Abstract
This paper proposes a federated framework for demand flexibility aggregation to support grid operations. Unlike existing geometric methods that rely on a static, pre-defined base set as the geometric template for aggregation, our framework establishes a true federated process by enabling the collaborative optimization of this base set without requiring the participants sharing sensitive data with the aggregator. Specifically, we first formulate the base set optimization problem as a bilevel program. Using optimal solution functions, we then reformulate the bilevel program into a single-level, unconstrained learning task. By exploiting the decomposable structure of the overall gradient, we further design a decentralized gradient-based algorithm to solve this learning task. The entire framework, encompassing base set optimization, aggregation, and disaggregation, operates by design without exchanging raw user data. Numerical results demonstrate that our proposed framework unlocks substantially more flexibility than the approaches with static base sets, thus providing a promising framework for efficient and privacy-enhanced approaches to coordinate demand flexibility at scale.

AI Insights

• The 100 % renewable goal forces demand‑flexibility aggregation to unite solar, wind, and EVs into a single controllable pool.
• Neural risk‑limiting dispatch generalizes across networks, keeping uncertainty bounded with minimal data.
• Model‑free self‑supervised learning dispatches DERs without labeled data, adapting quickly to changing profiles.
• Secure federated learning preserves raw consumption data while coordinating large‑scale flexibility.
• Geometric aggregation of thermostatically controlled loads yields compact polyhedral models for real‑time scheduling.
• Virtual power plants with temporal coupling constraints act as a single controllable resource across time.
• Recommended reading: Nocedal & Wright’s Numerical Optimization and Chalkis et al.’s billiard‑trajectory volume estimator.