The advancement of powerful yet opaque large language models (LLMs) necessitates a fundamental revision of the philosophical criteria used to evaluate artificial moral agents (AMAs). Pre-LLM frameworks often relied on the assumption of transparent architectures, which LLMs defy due to their stochastic outputs and opaque internal states. This paper argues that traditional ethical criteria are pragmatically obsolete for LLMs due to this mismatch. Engaging with core themes in the philosophy of technology, this paper proffers a revised set of ten functional criteria to evaluate LLM-based artificial moral agents: moral concordance, context sensitivity, normative integrity, metaethical awareness, system resilience, trustworthiness, corrigibility, partial transparency, functional autonomy, and moral imagination. These guideposts, applied to what we term "SMA-LLS" (Simulating Moral Agency through Large Language Systems), aim to steer AMAs toward greater alignment and beneficial societal integration in the coming years. We illustrate these criteria using hypothetical scenarios involving an autonomous public bus (APB) to demonstrate their practical applicability in morally salient contexts.

Recent years have seen significant advancements in designing reinforcement learning (RL)-based agents for building energy management. While individual success is observed in simulated or controlled environments, the scalability of RL approaches in terms of efficiency and generalization across building dynamics and operational scenarios remains an open question. In this work, we formally characterize the generalization space for the cross-environment, multi-objective building energy management task, and formulate the multi-objective contextual RL problem. Such a formulation helps understand the challenges of transferring learned policies across varied operational contexts such as climate and heat convection dynamics under multiple control objectives such as comfort level and energy consumption. We provide a principled way to parameterize such contextual information in realistic building RL environments, and construct a novel benchmark to facilitate the evaluation of generalizable RL algorithms in practical building control tasks. Our results show that existing multi-objective RL methods are capable of achieving reasonable trade-offs between conflicting objectives. However, their performance degrades under certain environment variations, underscoring the importance of incorporating dynamics-dependent contextual information into the policy learning process.

Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: i) deriving device behavior from design parameters, ii) simulating device performance, iii) finding the optimal candidate designs from simulations, iv) fabricating the optimal device, and v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems.

An increasing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality -- the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias in LLMs on the question-answering setting. Using inter-JBBQ to analyze GPT-4o and Swallow, we find that biased output varies according to its contexts even with the equal combination of social attributes.

As large language models (LLMs) gain popularity, their vulnerability to adversarial attacks emerges as a primary concern. While fine-tuning models on domain-specific datasets is often employed to improve model performance, it can inadvertently introduce vulnerabilities within the underlying model. In this work, we investigate Accidental Vulnerability, unexpected vulnerabilities arising from characteristics of fine-tuning data. We begin by identifying potential correlation factors such as linguistic features, semantic similarity, and toxicity across multiple experimental datasets. We then evaluate the adversarial robustness of these fine-tuned models, analyzing persona shifts and interpretability traits to understand how dataset factors contribute to attack success rates. Lastly, we explore causal relationships that offer new insights into adversarial defense strategies, highlighting the crucial role of dataset design in preserving model alignment. Our code is available at https://github.com/psyonp/accidental_vulnerability.

One of the goals of fairness research in NLP is to measure and mitigate stereotypical biases that are propagated by NLP systems. However, such work tends to focus on single axes of bias (most often gender) and the English language. Addressing these limitations, we contribute the first study of multilingual intersecting country and gender biases, with a focus on occupation recommendations generated by large language models. We construct a benchmark of prompts in English, Spanish and German, where we systematically vary country and gender, using 25 countries and four pronoun sets. Then, we evaluate a suite of 5 Llama-based models on this benchmark, finding that LLMs encode significant gender and country biases. Notably, we find that even when models show parity for gender or country individually, intersectional occupational biases based on both country and gender persist. We also show that the prompting language significantly affects bias, and instruction-tuned models consistently demonstrate the lowest and most stable levels of bias. Our findings highlight the need for fairness researchers to use intersectional and multilingual lenses in their work.

A Russian drone attack on a 25-storey residential building in Ukraine's capital, Kyiv, injured eight people, including a four-year-old girl, the head of the city's military administration, Tymur Tkachenko, said. The overnight attack was part of a barrage of "324 drones, four cruise missiles and three ballistic missiles", across the country, the Ukrainian Air Force said. The attack was focused on Starokostiantyniv, home to a major air base, the Air Force added. Ukraine's Air Force said it downed 309 drones and two missiles, while 15 drones and two missiles hit targets in three locations, without specifying where. The attack started a fire in Kropyvnytskyi, in central Ukraine, local officials said, but no injuries were reported.

Former Israeli Amb. to the U.S. Michael Oren discusses Iran's nuclear capabilities and negotiations with Hamas to release more hostages on'Fox Report.' The idea that Middle East peace cannot and should not advance without a formal agreement between Israel and the Palestinian Authority is outdated and demonstrably untrue. Indeed, it has done little but exacerbate conflict over the last 30 years and undermine U.S. interests in the region. They provide a new paradigm for peace between Israel and all of its neighbors, including the Palestinians. President Donald Trump obviously deserves the Nobel Peace Prize for breaking with the failed Oslo peace process paradigm still sanctified by legacy media and a bipartisan community of foreign policy elites, and for building new bridges of mutually-beneficial cooperation between Israel and its Arab neighbors.

White House'A.I. and crypto czar' David Sacks addresses the'major plank' in President Donald Trump's new A.I. action plan to dominate China on'The Story.' Artificial intelligence is no longer a niche tool for tech labs or science-fiction thrillers. It's now the battleground where the future of American power, prosperity, and freedom will be decided. With the release of "Winning the AI Race: America's AI Action Plan," the Trump administration is rightfully treating this moment as the 21st-century equivalent of the space race or the nuclear age. This bold strategy outlines over 90 policy actions that span three key pillars: Accelerating Innovation, Building American AI Infrastructure, and Leading in International Diplomacy and Security. Each of these pillars sends a clear message to the world: America intends to lead – not follow – on artificial intelligence. This is a race we can't afford to lose.

DroneUp CEO Tom Walker speaks with Fox News Digital about his Congressional testimony calling for a nationalized database of drone pilots and flights amid changing technology, while warning the country's airspace regulations are unprepared. As drone technology rapidly advances, industry experts are warning Congress about potential airspace lapses creating the next national security threat if left unregulated. In a U.S. House Homeland Security Subcommittee hearing held last week, drone industry experts testified about the looming threats to airspace safety posed by unmanned aircraft systems (UAS). "More than half of all near misses with commercial and general aviation are with drones," Tom Walker, CEO of DroneUp, told Fox News Digital. Drone experts are asking Congress for a centralized database to track flights and pilots in an attempt to fill gaps in airspace regulations.