Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems as rooftop PV and home energy management systems become pervasive, yet most existing market and reinforcement learning designs emphasize efficiency or private profit and offer little real-time guidance to ensure equitable outcomes under uncertainty. To address this gap, a fairness-aware multiagent reinforcement learning framework, FairMarket-RL, is proposed in which a large language model (LLM) critic shapes bidding policies within a continuous double auction under partial observability and discrete price-quantity actions. After each trading slot, the LLM returns normalized fairness scores Fairness-to-Grid (FTG), Fairness-Between-Sellers (FBS), and Fairness-of-Pricing (FPP) that are integrated into the reward via ramped coefficients and tunable scaling, so that fairness guidance complements, rather than overwhelms, economic incentives. The environment models realistic residential load and PV profiles and enforce hard constraints on prices, physical feasibility, and policy-update stability. Across a progression of experiments from a small pilot to a larger simulated community and a mixed-asset real-world dataset, the framework shifts exchanges toward local P2P trades, lowers consumer costs relative to grid-only procurement, sustains strong fairness across participants, and preserves utility viability. Sensitivity analyses over solar availability and aggregate demand further indicate robust performance, suggesting a scalable, LLM-guided pathway to decentralized electricity markets that are economically efficient, socially equitable, and technically sound.

Three major Japanese convenience store operators posted growth in their group operating revenues and profits in the March-May first quarter of the current business year, according to their earnings reports. Retail giant Seven & I Holdings, the operator of industry leader Seven-Eleven Japan, saw its mainstay overseas convenience store operations recover thanks to labor and other cost cuts. FamilyMart's operating profit grew 17.9% from a year before to 27.8 billion, as advertisements featuring Los Angeles Dodgers star Shohei Ohtani helped attract more customers and boost sales of onigiri rice balls. FamilyMart also attracted budget-minded consumers thanks to its discount sales of food items such as eggs and milk. As a result, the company's net profit jumped 36.7% to a record 21.1 billion.

Machine learning has been successful in building control policies to drive a complex system to desired states in various applications (e.g. games, robotics, etc.). To be specific, a number of parameters of policy can be automatically optimized from the observations of environment to be able to generate a sequence of decisions leading to the best performance. In this survey paper, we particularly explore such policy-learning techniques for another unique, practical use-case scenario--farming, in which critical decisions (e.g., water supply, heating, etc.) must be made in a timely manner to minimize risks (e.g., damage to plants) while maximizing the revenue (e.g., healthy crops) in the end. We first provide a broad overview of latest studies on it to identify not only domain-specific challenges but opportunities with potential solutions, some of which are suggested as promising directions for future research. Also, we then introduce our successful approach to being ranked second among 46 teams at the ''3rd Autonomous Greenhouse Challenge'' to use this specific example to discuss the lessons learned about important considerations for design to create autonomous farm-management systems.

The inaccurate translation of numbers can lead to significant security issues, ranging from financial setbacks to medical inaccuracies. While large language models (LLMs) have made significant advancements in machine translation, their capacity for translating numbers has not been thoroughly explored. This study focuses on evaluating the reliability of LLM-based machine translation systems when handling numerical data. In order to systematically test the numerical translation capabilities of currently open source LLMs, we have constructed a numerical translation dataset between Chinese and English based on real business data, encompassing ten types of numerical translation. Experiments on the dataset indicate that errors in numerical translation are a common issue, with most open-source LLMs faltering when faced with our test scenarios. Especially when it comes to numerical types involving large units like ``million", ``billion", and "yi", even the latest llama3.1 8b model can have error rates as high as 20%. Finally, we introduce three potential strategies to mitigate the numerical mistranslations for large units.

Aligning Large Language Models (LLMs) to human preferences through preference optimization has been crucial but labor-intensive, necessitating for each prompt a comparison of both a chosen and a rejected text completion by evaluators. Recently, Kahneman-Tversky Optimization (KTO) has demonstrated that LLMs can be aligned using merely binary "thumbs-up" or "thumbs-down" signals on each prompt-completion pair. In this paper, we present theoretical foundations to explain the successful alignment achieved through these binary signals. Our analysis uncovers a new perspective: optimizing a binary classifier, whose logit is a reward, implicitly induces minimizing the Direct Preference Optimization (DPO) loss. In the process of this discovery, we identified two techniques for effective alignment: reward shift and underlying distribution matching. Consequently, we propose a new algorithm, \textit{Binary Classifier Optimization}, that integrates the techniques. We validate our methodology in two settings: first, on a paired preference dataset, where our method performs on par with DPO and KTO; and second, on binary signal datasets simulating real-world conditions with divergent underlying distributions between thumbs-up and thumbs-down data. Our model consistently demonstrates effective and robust alignment across two base LLMs and three different binary signal datasets, showcasing the strength of our approach to learning from binary feedback.

Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the same user set while preserving data privacy. In production environment, VFL usually involves one task party and one data party. Fair and economically efficient feature trading is crucial to the commercialization of VFL, where the task party is considered as the data consumer who buys the data party's features. However, current VFL feature trading practices often price the data party's data as a whole and assume transactions occur prior to the performing VFL. Neglecting the performance gains resulting from traded features may lead to underpayment and overpayment issues. In this study, we propose a bargaining-based feature trading approach in VFL to encourage economically efficient transactions. Our model incorporates performance gain-based pricing, taking into account the revenue-based optimization objectives of both parties. We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives. Moreover, we develop performance gain estimation-based bargaining strategies for imperfect performance information scenarios and discuss potential security issues and solutions. Experiments on three real-world datasets demonstrate the effectiveness of the proposed bargaining model.

Growing lettuce with artificial intelligence (AI) in autonomous greenhouses, by algorithms developed in different parts of the world: today the young lettuce plants of the five international teams that compete in the two final rounds of the Autonomous Greenhouse Challenge were planted in the experimental greenhouses of Wageningen University & Research in Bleiswijk. The goal is to grow these lettuces fully autonomously with an AI algorithm on a cloud platform with good quality and little resource and energy use and without any human interference. The competition and teams' performance can be followed live on an online dashboard. Will the computer be able to complete a fully autonomous growing cycle? Five international teams located around the world will produce a lettuce crop using a fully autonomous algorithm during two growing cycles.

Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions? The standard approach based on Markowitz or other more or less sophisticated financial rules aims to find the best portfolio allocation thanks to forecasted expected returns and risk but fails to fully relate market conditions to hedging strategies decision. In contrast, Deep Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic dependency between market information and hedging strategies allocation decisions. In this paper, we present a realistic and augmented DRL framework that: (i) uses additional contextual information to decide an action, (ii) has a one period lag between observations and actions to account for one day lag turnover of common asset managers to rebalance their hedge, (iii) is fully tested in terms of stability and robustness thanks to a repetitive train test method called anchored walk forward training, similar in spirit to k fold cross validation for time series and (iv) allows managing leverage of our hedging strategy. Our experiment for an augmented asset manager interested in sizing and timing his hedges shows that our approach achieves superior returns and lower risk.

Detroit – Ford Motor Co. posted results Thursday that were not as grim as expected for its second quarter, during which its U.S. factories were shuttered for half the period to combat the spread of the novel coronavirus and car buyers sheltering in place. Ford reported a $1.12 billion (¥117.1 billion) second-quarter net profit, pushed into the black by a $3.5 billion gain on the value of its stake in the Argo AI autonomous vehicle operation. Without the one-time gain, the company lost $1.9 billion, or 35 cents per share. But that was far better than the $1.17 a share loss Wall Street had expected, according to FactSet. A year ago, Ford posted a $148 million net profit.

NAGOYA - Toyota Motor Corp. will cut summer bonuses for some 9,800 managers by 4 to 5 percent, as it looks to tighten cost control in the face of high spending on developing technology for autonomous and electrified vehicles, a source close to the matter said Thursday. The decision comes even as the company expects a 19.5 percent rise in net profit in the current fiscal year, and reflects an uncertain business outlook due to the prolonged trade war between the United States and China, the source said. Toyota President Akio Toyoda said Thursday at an annual shareholders' meeting that his company is boosting efforts in developing zero-emission vehicles including fuel cell vehicles. "We are facing a once-in-a-century transformation. I hope to build a mobility society of the future with our shareholders," Toyoda said at the meeting at its headquarters in Aichi Prefecture.