AI Summary

• Bayesian statistics are the natural framework for AI risk, allowing risk estimates to be continuously updated as models are evaluated and new behaviors are observed. [3]
• Bayesian Networks (BNs) are graphical models that help represent and quantify probabilistic relationships among a set of variables, making them excellent for modeling pathways to harm that involve multiple interacting factors. [3]
• Copulas are a powerful tool for modeling statistical interdependence without assuming causality, useful for modeling systemic or cascading risks where the failure of one component correlates with the failure of others. [3]
• Monte Carlo simulation: A computational technique that models uncertainty by running thousands of trials, sampling from probability distributions to generate a range of possible outcomes and their frequencies. [3]
• Bayesian statistics: A framework for updating prior knowledge (expert belief) with new evidence, enabling learning and continuous risk estimation. [3]
• Bayesian Networks (BNs): Graphical models representing probabilistic relationships among variables, integrating expert knowledge with data to model complex causal chains and update probabilities as new evidence becomes available. [3]
• Copulas: Functions that separate marginal probability distributions from the structure of statistical interdependence, useful for modeling systemic or cascading risks. [3]
• The lack of historical data for novel AI capabilities and failure modes is a significant challenge in quantifying AI risk. [2]
• Expert elicitation: A method used when empirical data is sparse, involving querying specialists on the likelihood of specific events. [1]

Abstract
Rapidly advancing artificial intelligence (AI) systems introduce novel, uncertain, and potentially catastrophic risks. Managing these risks requires a mature risk-management infrastructure whose cornerstone is rigorous risk modeling. We conceptualize AI risk modeling as the tight integration of (i) scenario building$-$causal mapping from hazards to harms$-$and (ii) risk estimation$-$quantifying the likelihood and severity of each pathway. We review classical techniques such as Fault and Event Tree Analyses, FMEA/FMECA, STPA and Bayesian networks, and show how they can be adapted to advanced AI. A survey of emerging academic and industry efforts reveals fragmentation: capability benchmarks, safety cases, and partial quantitative studies are valuable but insufficient when divorced from comprehensive causal scenarios. Comparing the nuclear, aviation, cybersecurity, financial, and submarine domains, we observe that every sector combines deterministic guarantees for unacceptable events with probabilistic assessments of the broader risk landscape. We argue that advanced-AI governance should adopt a similar dual approach and that verifiable, provably-safe AI architectures are urgently needed to supply deterministic evidence where current models are the result of opaque end-to-end optimization procedures rather than specified by hand. In one potential governance-ready framework, developers conduct iterative risk modeling and regulators compare the results with predefined societal risk tolerance thresholds. The paper provides both a methodological blueprint and opens a discussion on the best way to embed sound risk modeling at the heart of advanced-AI risk management.

Why we think this paper is great for you:
This paper directly addresses the critical need for risk management frameworks, aligning with concerns about the potential dangers of advanced AI systems. Understanding and mitigating these risks is a core interest within the broader field of AI safety and control.

AI Summary

• The document outlines a method for estimating the impact of Large Language Models (LLMs) on risk scenarios, using expert elicitation and benchmark tasks. [2]
• The process involves assigning indicators to each parameter in the risk model, then building a quantitative mapping between LLM capabilities and the values of the parameters. [1]

Abstract
Although general-purpose AI systems offer transformational opportunities in science and industry, they simultaneously raise critical concerns about safety, misuse, and potential loss of control. Despite these risks, methods for assessing and managing them remain underdeveloped. Effective risk management requires systematic modeling to characterize potential harms, as emphasized in frameworks such as the EU General-Purpose AI Code of Practice. This paper advances the risk modeling component of AI risk management by introducing a methodology that integrates scenario building with quantitative risk estimation, drawing on established approaches from other high-risk industries. Our methodology models risks through a six-step process: (1) defining risk scenarios, (2) decomposing them into quantifiable parameters, (3) quantifying baseline risk without AI models, (4) identifying key risk indicators such as benchmarks, (5) mapping these indicators to model parameters to estimate LLM uplift, and (6) aggregating individual parameters into risk estimates that enable concrete claims (e.g., X% probability of >\$Y in annual cyber damages). We examine the choices that underlie our methodology throughout the article, with discussions of strengths, limitations, and implications for future research. Our methodology is designed to be applicable to key systemic AI risks, including cyber offense, biological weapon development, harmful manipulation, and loss-of-control, and is validated through extensive application in LLM-enabled cyber offense. Detailed empirical results and cyber-specific insights are presented in a companion paper.

Why we think this paper is great for you:
The paper proposes a specific methodology for quantifying AI risk, which is a vital step in developing effective safeguards. This aligns with the user's interest in proactively managing potential harms from AI systems.

AI Summary

• The main algorithm achieves a regret bound of O(K⋆√dT), optimal up to a O(√K⋆) factor and logarithmic terms. [3]
• Disagreement coefficient: A measure of how much the aggregate demand function changes when a single breakpoint is moved. [3]
• Regret bound: An upper bound on the difference between the algorithm's revenue and the optimal revenue. [3]
• The paper introduces contextual dynamic pricing with heterogeneous buyers, where the goal is to maximize revenue by adapting prices based on buyer types and contexts. [2]

Abstract
We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly posts prices (over $T$ rounds) that depend on the observable $d$-dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support size $K_{\star}$. We develop a contextual pricing algorithm based on optimistic posterior sampling with regret $\widetilde{O}(K_{\star}\sqrt{dT})$, which we prove to be tight in $d$ and $T$ up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware zooming algorithm that achieves the optimal dependence on $K_{\star}$.

Why we think this paper is great for you:
The research focuses on dynamic pricing strategies, a key component of supply chain optimization and pricing strategies. Understanding how pricing adapts to different buyer behaviors is highly relevant to the user's interests.

AI Summary

• Deep learning and big data analytics are being used to improve transportation efficiency, safety, and sustainability. [3]
• Transportation management is evolving with the help of artificial intelligence (AI) and Internet of Things (IoT). [2]
• Data privacy and security concerns [1]

Abstract
The theoretical landscape of transportation cost planning is shifting from deterministic linear models to dynamic, data-driven optimization. As supply chains face volatility, static 20th-century cost assumptions prove increasingly inadequate. Despite rapid technological advancements, a unified framework linking economic production theory with the operational realities of autonomous, sustainable logistics remains absent. Existing models fail to address non-linear stepwise costs and real-time stochastic variables introduced by market dynamics. This study reconstructs transportation cost planning theory by synthesizing Grand, Middle-Range, and Applied theories. It aims to integrate stepwise cost functions, AI-driven decision-making, and environmental externalities into a cohesive planning model. A systematic theoretical synthesis was conducted using 28 high-impact papers published primarily between 2018 and 2025, employing multi-layered analysis to reconstruct cost drivers. The study identifies three critical shifts: the transition from linear to stepwise fixed costs, the necessity of AI-driven dynamic pricing for revenue optimization, and the role of Autonomous Electric Vehicles (AEVs) in minimizing long-term marginal costs. A "Dynamic-Sustainable Cost Planning Theory" is proposed, arguing that cost efficiency now depends on algorithmic prediction and autonomous fleet utilization rather than simple distance minimization.

Why we think this paper is great for you:
This paper tackles the evolving landscape of transportation cost planning, incorporating dynamic pricing and sustainability – areas directly related to supply chain efficiency and optimization.

AI Summary

• The paper discusses the concept of agentic AI and its applications in various domains. [3]
• Agentic AI refers to autonomous intelligence that can perform complex tasks and make decisions on its own. [3]
• Agentic AI: Autonomous intelligence that can perform complex tasks and make decisions on its own. [3]
• The authors propose a framework for integrating large language models (LLMs) with blockchain smart contracts using Model Context Protocol (MCP). [2]
• The paper highlights the need for evaluating AI reasoning models in pediatric medicine and discusses the comparative analysis of O3-Mini and O3-Mini-High models. [1]

Abstract
Agentic AI marks a major shift in how autonomous systems reason, plan, and execute multi-step tasks. Unlike traditional single model prompting, agentic workflows integrate multiple specialized agents with different Large Language Models(LLMs), tool-augmented capabilities, orchestration logic, and external system interactions to form dynamic pipelines capable of autonomous decision-making and action. As adoption accelerates across industry and research, organizations face a central challenge: how to design, engineer, and operate production-grade agentic AI workflows that are reliable, observable, maintainable, and aligned with safety and governance requirements. This paper provides a practical, end-to-end guide for designing, developing, and deploying production-quality agentic AI systems. We introduce a structured engineering lifecycle encompassing workflow decomposition, multi-agent design patterns, Model Context Protocol(MCP), and tool integration, deterministic orchestration, Responsible-AI considerations, and environment-aware deployment strategies. We then present nine core best practices for engineering production-grade agentic AI workflows, including tool-first design over MCP, pure-function invocation, single-tool and single-responsibility agents, externalized prompt management, Responsible-AI-aligned model-consortium design, clean separation between workflow logic and MCP servers, containerized deployment for scalable operations, and adherence to the Keep it Simple, Stupid (KISS) principle to maintain simplicity and robustness. To demonstrate these principles in practice, we present a comprehensive case study: a multimodal news-analysis and media-generation workflow. By combining architectural guidance, operational patterns, and practical implementation insights, this paper offers a foundational reference to build robust, extensible, and production-ready agentic AI workflows.

Why we think this paper is great for you:
The paper’s focus on agentic AI workflows – integrating multiple AI agents – is a significant advancement in autonomous system design, aligning with the user's interest in sophisticated AI applications.

AI Summary

• SWE-Bench: a comprehensive benchmark to evaluate autonomous code-writing and code-fixing agents on realistic tasks. [3]
• The combination of monorepo development and LLM-based tools like ECO underscores a trend toward holistic scale: treating an entire organization’s code as a single evolvable system, with AI agents providing the intelligence to manage global changes, dependency analysis, and performance tuning in ways humans alone could not easily scale. [2]
• Large-scale software engineering has driven interest in AI assistance for code discovery, understanding, and consistent changes at scale. [1]

Abstract
Real-world AI software engineering demands coding agents that can reason over massive repositories, maintain durable memory across and within long sessions, and robustly coordinate complex toolchains at test time. Existing open-source coding agents provide transparency but frequently fall short when pushed to these industrial-scale workloads, while proprietary coding agents offer strong practical performance but limited extensibility, interpretability, and controllability. We present the Confucius Code Agent (CCA), an open-sourced AI software engineer that can operate at an industrial scale. CCA is built atop the Confucius SDK, an open-sourced agent development platform designed around three complementary perspectives: Agent Experience (AX), User Experience (UX), and Developer Experience (DX). The SDK introduces a unified orchestrator with hierarchical working memory for long-context reasoning, a persistent note-taking system for cross-session continual learning, and a modular extension module for robust tool use. Moreover, a meta-agent automates the synthesis, evaluation, and refinement of agent configurations through a build-test-improve loop, enabling rapid agent development on new tasks, environments, and tool stacks. Instantiated on Confucius SDK with these mechanisms, CCA delivers strong performance on real-world software engineering tasks. On SWE-Bench-Pro, CCA achieves a state-of-the-art Resolve@1 performance of 54.3%, substantially improving over prior coding agents. Together, the Confucius SDK and CCA provide a transparent, extensible, and reproducible foundation for AI agents, bridge gaps between research prototypes and production-grade systems, and support agent development and deployment at industrial scale.

Why we think this paper is great for you:
This research explores large-scale coding agents, which are crucial for automating software development – a core component of supply chain and operational efficiency.

AI Summary

• The paper discusses the valuation of energy storage using utility indifference pricing (UIP) and stochastic dual dynamic programming (SDDP). [2]

Abstract
This paper applies computational techniques of convex stochastic optimization to optimal operation and valuation of electricity storages in the face of uncertain electricity prices. Our valuations are based on the indifference pricing principle, which builds on optimal trading strategies and calibrates to the user's financial position, market views and risk preferences. The underlying optimization problem is solved with the Stochastic Dual Dynamic Programming algorithm which is applicable to various specifications of storages, and it allows for e.g. hard constraints on storage capacity and charging speed. We illustrate the approach in intraday trading where the agent charges or discharges a battery over a finite number of delivery periods, and the electricity prices are subject to bid-ask spreads and significant uncertainty. Optimal strategies are found in a matter of minutes on a regular PC. We find that the corresponding trading strategies and battery valuations vary consistently with respect to the agent's risk preferences as well as the physical characteristics of the battery.

Why we think this paper is great for you:
The paper’s focus on electricity storage and intraday markets provides insights into optimizing energy systems, a critical area for sustainable supply chain operations and resource management.