The wreck was like a bug on the wall, a jumbly shape splayed on the abyssal plain. It was noticed by a team of autonomous-underwater-vehicle operators on board a subsea exploration vessel, working at an undisclosed location in the Atlantic Ocean, about a thousand miles from the nearest shore. The analysts belonged to a small private company that specializes in deep-sea search operations; I have been asked not to name it. They were looking for something else. In the past decade, the company has helped to transform the exploration of the seabed by deploying fleets of A.U.V.s--underwater drones--which cruise in formation, mapping large areas of the ocean floor with high-definition imagery.

Israel launched its first-ever strikes against Houthi rebels in Yemen just days after Jerusalem vowed revenge for a drone strike on Tel Aviv. Russia has been aiding the Houthis' assault on Western shipping lanes in the Red Sea by providing them targeting data. As the Houthis ramped up their strikes on the U.S. and other nations' postures in the region after the Oct. 7 attack on Israel, Russians offered satellite data allowing them to expand their strikes, take out multimillion-dollar U.S. drones and hit ships sailing through the Red Sea and the Suez Canal, through which 12% of global trade passes, according to a Wall Street Journal report. Each munition used to intercept a Houthi strike costs the U.S. upwards of between 1 million and 4 million. The data passed through Iran's Revolutionary Guard Corps (IRGC).

Soft robots offer adaptability and safe interaction with complex environments. Rapid prototyping kits that allow soft robots to be assembled easily will allow different geometries to be explored quickly to suit different environments or to mimic the motion of biological organisms. We introduce SoftSnap modules: snap-together components that enable the rapid assembly of a class of untethered soft robots. Each SoftSnap module includes embedded computation, motor-driven string actuation, and a flexible thermoplastic polyurethane (TPU) printed structure capable of deforming into various shapes based on the string configuration. These modules can be easily connected with other SoftSnap modules or customizable connectors. We demonstrate the versatility of the SoftSnap system through four configurations: a starfish-like robot, a brittle star robot, a snake robot, a 3D gripper, and a ring-shaped robot. These configurations highlight the ease of assembly, adaptability, and functional diversity of the SoftSnap modules. The SoftSnap modular system offers a scalable, snap-together approach to simplifying soft robot prototyping, making it easier for researchers to explore untethered soft robotic systems rapidly.

This essay examines how Artificial Intelligence (AI) systems are becoming more integral to international affairs by affecting how global governors exert power and pursue digital sovereignty. We first introduce a taxonomy of multifaceted AI payoffs for governments and corporations related to instrumental, structural, and discursive power in the domains of violence, markets, and rights. We next leverage different institutional and practice perspectives on sovereignty to assess how digital sovereignty is variously implicated in AI-empowered global governance. States both seek sovereign control over AI infrastructures in the institutional approach, while establishing sovereign competence through AI infrastructures in the practice approach. Overall, we present the digital sovereignty stakes of AI as related to entanglements of public and private power. Rather than foreseeing technology companies as replacing states, we argue that AI systems will embed in global governance to create dueling dynamics of public/private cooperation and contestation. We conclude with sketching future directions for IR research on AI and global governance.

Attacking fairness is crucial because compromised models can introduce biased outcomes, undermining trust and amplifying inequalities in sensitive applications like hiring, healthcare, and law enforcement. This highlights the urgent need to understand how fairness mechanisms can be exploited and to develop defenses that ensure both fairness and robustness. We introduce BadFair, a novel backdoored fairness attack methodology. BadFair stealthily crafts a model that operates with accuracy and fairness under regular conditions but, when activated by certain triggers, discriminates and produces incorrect results for specific groups. This type of attack is particularly stealthy and dangerous, as it circumvents existing fairness detection methods, maintaining an appearance of fairness in normal use. Our findings reveal that BadFair achieves a more than 85% attack success rate in attacks aimed at target groups on average while only incurring a minimal accuracy loss. Moreover, it consistently exhibits a significant discrimination score, distinguishing between pre-defined target and non-target attacked groups across various datasets and models.

Precipitation from tropical cyclones (TCs) can cause disasters such as flooding, mudslides, and landslides. Predicting such precipitation in advance is crucial, giving people time to prepare and defend against these precipitation-induced disasters. Developing deep learning (DL) rainfall prediction methods offers a new way to predict potential disasters. However, one problem is that most existing methods suffer from cumulative errors and lack physical consistency. Second, these methods overlook the importance of meteorological factors in TC rainfall and their integration with the numerical weather prediction (NWP) model. Therefore, we propose Tropical Cyclone Precipitation Diffusion (TCP-Diffusion), a multi-modal model for global tropical cyclone precipitation forecasting. It forecasts TC rainfall around the TC center for the next 12 hours at 3 hourly resolution based on past rainfall observations and multi-modal environmental variables. Adjacent residual prediction (ARP) changes the training target from the absolute rainfall value to the rainfall trend and gives our model the ability of rainfall change awareness, reducing cumulative errors and ensuring physical consistency. Considering the influence of TC-related meteorological factors and the useful information from NWP model forecasts, we propose a multi-model framework with specialized encoders to extract richer information from environmental variables and results provided by NWP models. The results of extensive experiments show that our method outperforms other DL methods and the NWP method from the European Centre for Medium-Range Weather Forecasts (ECMWF).

SpaceX has launched its fifth Starship test flight from Texas and returned the rocket's towering first-stage booster back to land for the first time, achieving a novel recovery method involving large metal arms. The rocket's Super Heavy first-stage booster lifted off at 7:25 am (12:25 GMT) on Sunday from SpaceX's launch facilities in Boca Chica, Texas, sending the second-stage Starship rocket on a path in space bound for the Indian Ocean west of Australia, where it will attempt atmospheric reentry followed by a water landing. The Super Heavy booster, after separating from the Starship booster some 74km (46 miles) in altitude, returned to the same area from which it was launched to make its landing attempt, aided by two robotic arms attached to the launch tower. "The tower has caught the rocket!!" SpaceX founder Elon Musk posted on X. Towering almost 121 metres (400 feet), the empty Starship arched over the Gulf of Mexico like the four Starships before it that ended up being destroyed, either soon after liftoff or while ditching into the sea. The last one in June was the most successful yet, completing its flight without exploding.

Complex systems can undergo critical transitions, where slowly changing environmental conditions trigger a sudden shift to a new, potentially catastrophic state. Early warning signals for these events are crucial for decision-making in fields such as ecology, biology and climate science. Generic early warning signals motivated by dynamical systems theory have had mixed success on real noisy data. More recent studies found that deep learning classifiers trained on synthetic data could improve performance. However, neither of these methods take advantage of historical, system-specific data. Here, we introduce an approach that trains machine learning classifiers directly on surrogate data of past transitions, namely surrogate data-based machine learning (SDML). The approach provides early warning signals in empirical and experimental data from geology, climatology, sociology, and cardiology with higher sensitivity and specificity than two widely used generic early warning signals -- variance and lag-1 autocorrelation. Since the approach is trained directly on surrogates of historical data, it is not bound by the restricting assumption of a local bifurcation like previous methods. This system-specific approach can contribute to improved early warning signals to help humans better prepare for or avoid undesirable critical transitions.

Retrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources. The increasing capacity of LLMs to process longer input sequences opens up avenues for providing more retrieved information, to potentially enhance the quality of generated outputs. It is plausible to assume that a larger retrieval set would contain more relevant information (higher recall), that might result in improved performance. However, our empirical findings demonstrate that for many long-context LLMs, the quality of generated output initially improves first, but then subsequently declines as the number of retrieved passages increases. This paper investigates this phenomenon, identifying the detrimental impact of retrieved "hard negatives" as a key contributor. To mitigate this and enhance the robustness of long-context LLM-based RAG, we propose both training-free and training-based approaches. We first showcase the effectiveness of retrieval reordering as a simple yet powerful training-free optimization. Furthermore, we explore training-based methods, specifically RAG-specific implicit LLM fine-tuning and RAG-oriented fine-tuning with intermediate reasoning, demonstrating their capacity for substantial performance gains. Finally, we conduct a systematic analysis of design choices for these training-based methods, including data distribution, retriever selection, and training context length.

This paper investigates the limitations of transformers for entity-tracking tasks in large language models. We identify a theoretical constraint, showing that transformers require at least $\log_2 (n+1)$ layers to handle entity tracking with $n$ state changes. To address this issue, we propose an efficient and frugal enhancement to the standard attention mechanism, enabling it to manage long-term dependencies more efficiently. By considering attention as an adjacency matrix, our model can track entity states with a single layer. Empirical results demonstrate significant improvements in entity tracking datasets while keeping competitive performance on standard natural language modeling. Our modified attention allows us to achieve the same performance with drastically fewer layers. Additionally, our enhanced mechanism reveals structured internal representations of attention. Extensive experiments on both toy and complex datasets validate our approach. Our contributions include theoretical insights, an improved attention mechanism, and empirical validation.