AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consumer markets? To address these questions, we develop an experimental framework that evaluates the performance of various LLM agents in real-world negotiation and transaction settings. Our findings reveal that AI-mediated deal-making is an inherently imbalanced game -- different agents achieve significantly different outcomes for their users. Moreover, behavioral anomalies in LLMs can result in financial losses for both consumers and merchants, such as overspending or accepting unreasonable deals. These results underscore that while automation can improve efficiency, it also introduces substantial risks. Users should exercise caution when delegating business decisions to AI agents.

Modern operational landscapes, spanning domains such as retail, healthcare, finance, and software systems, are increasingly characterized by complex interdependencies and massive data streams. In such settings, anomalies rarely arise from a single isolated factor; rather, they emerge as the cumulative effect of multi-hop causal chains. Existing RCA methods typically focus on detecting outliers or isolating single nodes based on correlation or localized attribution. However, these approaches do not provide a complete explanation of why a failure occurred. In other words they do not systematically trace all possible causal pathways from an observed effect back to its initial triggers. The primary motivation for our work is to address this limitation by developing a package that systematically reconstructs the full causal pathway from an observed anomaly back to its root cause. By leveraging the strengths of the DoWhy causal inference library, our method extends existing techniques to not only identify individual anomalous nodes but also trace entire multi-hop causal chains. This end-to-end approach enables practitioners to intervene precisely at the earliest disruption points, thereby reducing the risk of recurring failures and improving overall system reliability.

According to statistics from Shenzhen New Strategy Media's Industrial Research Institute, there were over 160 humanoid-robot manufacturers worldwide as of June 2024, of which more than 60 were in China, more than 30 in the United States, and about 40 in Europe. In addition to having the largest number of manufacturers, China stands out for the way its EV sector is backing most of these robotics companies. Thanks in part to substantial government subsidies and concerted efforts from the tech sector, China has emerged as the world's largest EV market and manufacturer. In 2024, 54% of cars sold in China were electric or hybrid, compared with 8% in the US. China also became the first nation to reach an annual production of 10 million "new energy vehicles" (NEVs), a category that includes all vehicles powered partly or entirely by electricity.

When Jensen Huang spoke at the Nvidia annual general meeting last week, he made no mention of a share price slide. The US chipmaker, buoyed up by its key role in the artificial intelligence boom, had briefly become the world's most valuable company on 18 June but the crown slipped quickly. Nvidia shed about 550bn ( 434bn) from the 3.4tn ( 2.68tn) peak market value it had reached that week, as tech investors, combining profit-taking with doubts about the sustainability of its rocketing growth, applied the brakes. Huang, however, spoke like the CEO of a business that took 30 days this year to go from a valuation of 2tn to 3tn – and sees 4tn coming into view. He described a forthcoming group of powerful new chips, called Blackwell, as potentially "the most successful product in our history" and perhaps in the entire history of the computer.

Constructing high-quality query-response pairs from custom corpus is crucial for supervised fine-tuning (SFT) large language models (LLMs) in many applications, like creating domain-specific AI assistants or roleplaying agents. However, sourcing this data through human annotation is costly, and existing automated methods often fail to capture the diverse range of contextual granularity and tend to produce homogeneous data. To tackle these issues, we introduce a novel method named AugCon, capable of automatically generating context-driven SFT data across multiple levels of granularity with high diversity, quality and fidelity. AugCon begins by generating queries using the Context-Split-Tree (CST), an innovative approach for recursively deriving queries and splitting context to cover full granularity. Then, we train a scorer through contrastive learning to collaborate with CST to rank and refine queries. Finally, a synergistic integration of self-alignment and self-improving is introduced to obtain high-fidelity responses. Extensive experiments are conducted incorporating both human and automatic evaluations, encompassing a test scenario and four widely-used benchmarks in English and Chinese. The results highlight the significant advantages of AugCon in producing high diversity, quality, and fidelity SFT data against several state-of-the-art methods. All of our code, dataset, and fine-tuned model will be available at: https://github.com/quanshr/AugCon.

This paper presents a novel approach to optimizing profit margins in non-life insurance markets through a gradient descent-based method, targeting three key objectives: 1) maximizing profit margins, 2) ensuring conversion rates, and 3) enforcing fairness criteria such as demographic parity (DP). Traditional pricing optimization, which heavily lean on linear and semi definite programming, encounter challenges in balancing profitability and fairness. These challenges become especially pronounced in situations that necessitate continuous rate adjustments and the incorporation of fairness criteria. Specifically, indirect Ratebook optimization, a widely-used method for new business price setting, relies on predictor models such as XGBoost or GLMs/GAMs to estimate on downstream individually optimized prices. However, this strategy is prone to sequential errors and struggles to effectively manage optimizations for continuous rate scenarios. In practice, to save time actuaries frequently opt for optimization within discrete intervals (e.g., range of [-20\%, +20\%] with fix increments) leading to approximate estimations. Moreover, to circumvent infeasible solutions they often use relaxed constraints leading to suboptimal pricing strategies. The reverse-engineered nature of traditional models complicates the enforcement of fairness and can lead to biased outcomes. Our method addresses these challenges by employing a direct optimization strategy in the continuous space of rates and by embedding fairness through an adversarial predictor model. This innovation not only reduces sequential errors and simplifies the complexities found in traditional models but also directly integrates fairness measures into the commercial premium calculation. We demonstrate improved margin performance and stronger enforcement of fairness highlighting the critical need to evolve existing pricing strategies.

Robots have mastered sight and sound, and new technology is helping them fine-tune their sense of touch for a surprising use case. With a background in electronics, engineering, and bionanotechnology, Dr. Atif Syed was fascinated with nano-scale devices that can have a massive impact on processes. This focus led Syed, the CEO of Wootzano, a UK-based robotics company, to create an electronic skin for robots that enables awareness of pressure sensitive contact. "I knew that one of the biggest issues was giving robots the sense of real touch like humans have," he explains. "The electronic skin sensors can feel how much force is applied and the exact direction of a motion. The most interesting part is that this capability is on a completely stretchable material."

The chaos at ports continues with no end. A troubling realization is sinking into the mind: The effects of the " Great Supply Chain Distortion" are already being felt throughout the country. For example, 30% of baby formula brands may be out of stock soon. This could cause retailers to limit the number of containers they can sell and leave parents concerned that their children won't get enough food. This issue covers all industries and has an impact on automotive, healthcare IT, hospitality, manufacturing, apparel, as well as other areas.

In an office park in Hawthorne, a robot built by rocket scientists is making pizza. On a conveyor belt, a nozzle spits out sauce, dispensers shake cheese and toppings on top, then a robotic lift carries the raw pie to one of four 900-degree deck ovens. Cameras and sensors track the progress from step to step, making tiny adjustments along the way. In 45 seconds, a finished pizza pops out. It costs just $7 to order (or as much as $10, depending on toppings).

The number of smartphone users across the world has skyrocketed over the last decade and promises to do so in the future too. Additionally, most business functions can now be executed on mobile devices. However, despite the mobile surge, telecom operators around the world are still not that profitable, with average net profit margins hovering around the 17% mark. The main reasons for the middling profit rates are the high number of market rivals vouching for the same customer base and the high overhead expenses associated with the sector. Communication Service Providers (CSPs) need to become more data-driven to reduce such costs and, automatically, improve their profit margins.