The presence of autonomous systems is growing at a fast pace and it is impacting many aspects of our lives. Designed to learn and act independently, these systems operate and perform decision-making without human intervention. However, they lack the ability to incorporate users' ethical preferences, which are unique for each individual in society and are required to personalize the decision-making processes. This reduces user trust and prevents autonomous systems from behaving according to the moral beliefs of their end-users. When multiple systems interact with differing ethical preferences, they must negotiate to reach an agreement that satisfies the ethical beliefs of all the parties involved and adjust their behavior consequently. To address this challenge, this paper proposes RobEthiChor, an approach that enables autonomous systems to incorporate user ethical preferences and contextual factors into their decision-making through ethics-based negotiation. RobEthiChor features a domain-agnostic reference architecture for designing autonomous systems capable of ethic-based negotiating. The paper also presents RobEthiChor-Ros, an implementation of RobEthiChor within the Robot Operating System (ROS), which can be deployed on robots to provide them with ethics-based negotiation capabilities. To evaluate our approach, we deployed RobEthiChor-Ros on real robots and ran scenarios where a pair of robots negotiate upon resource contention. Experimental results demonstrate the feasibility and effectiveness of the system in realizing ethics-based negotiation. RobEthiChor allowed robots to reach an agreement in more than 73% of the scenarios with an acceptable negotiation time (0.67s on average). Experiments also demonstrate that the negotiation approach implemented in RobEthiChor is scalable.

Moral judgment is integral to large language models' (LLMs) social reasoning. As multi-agent systems gain prominence, it becomes crucial to understand how LLMs function when collaborating compared to operating as individual agents. In human moral judgment, group deliberation leads to a Utilitarian Boost: a tendency to endorse norm violations that inflict harm but maximize benefits for the greatest number of people. We study whether a similar dynamic emerges in multi-agent LLM systems. We test six models on well-established sets of moral dilemmas across two conditions: (1) Solo, where models reason independently, and (2) Group, where they engage in multi-turn discussions in pairs or triads. In personal dilemmas, where agents decide whether to directly harm an individual for the benefit of others, all models rated moral violations as more acceptable when part of a group, demonstrating a Utilitarian Boost similar to that observed in humans. However, the mechanism for the Boost in LLMs differed: While humans in groups become more utilitarian due to heightened sensitivity to decision outcomes, LLM groups showed either reduced sensitivity to norms or enhanced impartiality. We report model differences in when and how strongly the Boost manifests. We also discuss prompt and agent compositions that enhance or mitigate the effect. We end with a discussion of the implications for AI alignment, multi-agent design, and artificial moral reasoning. Code available at: https://github.com/baltaci-r/MoralAgents

Generative models in function spaces, situated at the intersection of generative modeling and operator learning, are attracting increasing attention due to their immense potential in diverse scientific and engineering applications. While functional generative models are theoretically domain- and discretization-agnostic, current implementations heavily rely on the Fourier Neural Operator (FNO), limiting their applicability to regular grids and rectangular domains. To overcome these critical limitations, we introduce the Mesh-Informed Neural Operator (MINO). By leveraging graph neural operators and cross-attention mechanisms, MINO offers a principled, domain- and discretization-agnostic backbone for generative modeling in function spaces. This advancement significantly expands the scope of such models to more diverse applications in generative, inverse, and regression tasks. Furthermore, MINO provides a unified perspective on integrating neural operators with general advanced deep learning architectures. Finally, we introduce a suite of standardized evaluation metrics that enable objective comparison of functional generative models, addressing another critical gap in the field.

Machine learning techniques offer an effective approach to modeling dynamical systems solely from observed data. However, without explicit structural priors -- built-in assumptions about the underlying dynamics -- these techniques typically struggle to generalize to aspects of the dynamics that are poorly represented in the training data. Here, we demonstrate that reservoir computing -- a simple, efficient, and versatile machine learning framework often used for data-driven modeling of dynamical systems -- can generalize to unexplored regions of state space without explicit structural priors. First, we describe a multiple-trajectory training scheme for reservoir computers that supports training across a collection of disjoint time series, enabling effective use of available training data. Then, applying this training scheme to multistable dynamical systems, we show that RCs trained on trajectories from a single basin of attraction can achieve out-of-domain generalization by capturing system behavior in entirely unobserved basins.

A major problem in the study of large language models is to understand their inherent low-dimensional structure. We introduce an approach to study the low-dimensional structure of language models at a model-agnostic level: as sequential probabilistic models. We first empirically demonstrate that a wide range of modern language models exhibit low-rank structure: in particular, matrices built from the model's logits for varying sets of prompts and responses have low approximate rank. We then show that this low-rank structure can be leveraged for generation -- in particular, we can generate a response to a target prompt using a linear combination of the model's outputs on unrelated, or even nonsensical prompts. On the theoretical front, we observe that studying the approximate rank of language models in the sense discussed above yields a simple universal abstraction whose theoretical predictions parallel our experiments. We then analyze the representation power of the abstraction and give provable learning guarantees.

While continuous diffusion has shown remarkable success in continuous domains such as image generation, its direct application to discrete data has underperformed compared to purely discrete formulations. This gap is counterintuitive, given that continuous diffusion learns score functions that enable joint evolution across multiple positions. To understand this gap, we introduce token identifiability as an analytical framework for understanding how Gaussian noise corrupts discrete data through two mechanisms: discrete identity corruption and continuous rank degradation. We reveal that these mechanisms scale differently with vocabulary size, creating a temporal dissonance: at noise levels where discrete corruption preserves enough structure for conditional learning, continuous denoising is trivial; at noise levels where continuous denoising is meaningful, discrete corruption destroys nearly all conditional structure. To solve this, we propose CANDI (Continuous ANd DIscrete diffusion), a hybrid framework that decouples discrete and continuous corruption, enabling simultaneous learning of both conditional structure and continuous geometry. We empirically validate the temporal dissonance phenomenon and demonstrate that CANDI successfully avoids it. This unlocks the benefits of continuous diffusion for discrete spaces: on controlled generation, CANDI enables classifier-based guidance with off-the-shelf classifiers through simple gradient addition; on text generation, CANDI outperforms masked diffusion at low NFE, demonstrating the value of learning continuous gradients for discrete spaces. We include the code on the project page available here: https://patrickpynadath1.github.io/candi-lander

Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? Trump-Xi meeting: What's at stake and who has the upper hand? United States President Donald Trump expects "a lot of problems" will be solved between Washington and Beijing when he meets China's President Xi Jinping in South Korea for a high-stakes meeting on Thursday, amid growing trade tensions between the two. Relations between the two world powers have been strained in recent years, with Washington and Beijing imposing tit-for-tat trade tariffs topping 100 percent against each other this year, the US restricting its exports of semiconductors vital for artificial intelligence (AI) development and Beijing restricting exports of critical rare-earth metals which are vital for the defence industry and also the development of AI, among other issues. On the sidelines of the Asia-Pacific Economic Cooperation (APEC) summit in Gyeongju, South Korea, on Wednesday, Trump said an expected trade deal between China and the US would be good for both countries and "something very exciting for everybody".

As President Trump flew to South Korea on Wednesday to prepare for a summit with the Chinese leader, Xi Jinping, he made some remarks that set off alarm bells among Washington officials concerned about America's rivalry with China. "We'll be speaking about Blackwell," Mr. Trump said of his meeting with Mr. Xi, referring to the most advanced artificial intelligence chip from the U.S. chipmaker Nvidia. Mr. Trump called the technology a "super duper chip"; complimented Nvidia's chief executive, Jensen Huang; and declared, "We're about 10 years ahead of anybody else in chips." Mr. Trump's comments signaled a major potential change for U.S. policy that many Washington officials warn poses a national security risk. Selling such advanced A.I. chips to China is currently banned, and U.S. officials have worked for years to restrain Beijing's access to the cutting-edge technology.

This drone's wingspan rivals a 737--but it's lighter than a NFL linebacker It could deliver internet to remote areas...or quietly watch us from the stratosphere. Radical's Evenstar solar-powered drone has a 120-foot wing span and weighs just 240 pounds. Breakthroughs, discoveries, and DIY tips sent every weekday. Back in the mid-2010s, some of the world's biggest tech companies were racing to launch lightweight, solar-powered drones to hover above remote areas and beam down internet connectivity. Meta (then called Facebook) and Google, the two companies most heavily investing in the technology at the time, abruptly exited the space following a series of mishaps.

Human-level AI is still some ways off. Even the best artificial intelligence agents are fairly hopeless at online freelance work, according to an experiment that challenges the idea of AI replacing office workers en masse. The Remote Labor Index, a new benchmark developed by researchers at data annotation company Scale AI and the Center for AI Safety (CAIS), a nonprofit, measures the ability of frontier AI models to automate economically valuable work. The researchers gave several leading AI agents a range of simulated freelance work and found that even the best could perform less than 3 percent of the work, earning $1,810 out of a possible $143,991. The researchers looked at several tools and found the most capable to be Manus from a Chinese startup of the same name, followed by Grok from xAI, Claude from Anthropic, ChatGPT from OpenAI, and Gemini from Google.