Russia started producing a new long-range attack drone called the Garpiya-A1 last year using Chinese engines and parts, which it has deployed in the war in Ukraine, according to two sources from a European intelligence agency and documents seen by Reuters. The intelligence -- which included a production contract for the new drone, company correspondence on the manufacturing process and financial documents -- indicated that IEMZ Kupol, a subsidiary of Russian state-owned weapons-maker Almaz-Antey, produced more than 2,500 Garpiyas from July 2023 to July 2024. The existence of the new Russian drone incorporating Chinese technology has not been previously reported. IEMZ Kupol and Almaz-Antey did not respond to requests for comment. The two intelligence sources said that the Garpiya, which means Harpy in Russian, has been deployed against military and civilian targets in Ukraine, causing damage to critical infrastructure as well as both civilian and military casualties.

This paper examines the governance of multimodal large language models (MM-LLMs) through individual and collective deliberation, focusing on analyses of politically sensitive videos. We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings show that while experts emphasized emotion and narrative, the general public prioritized factual clarity, objectivity of the situation, and emotional neutrality. Additionally, we explored the impact of different governance mechanisms: quadratic vs. weighted ranking voting and equal vs. 20-80 power distributions on users decision-making on how AI should behave. Specifically, quadratic voting enhanced perceptions of liberal democracy and political equality, and participants who were more optimistic about AI perceived the voting process to have a higher level of participatory democracy. Our results suggest the potential of applying DAO mechanisms to help democratize AI governance.

Agent-based modeling (ABM) seeks to understand the behavior of complex systems by simulating a collection of agents that act and interact within an environment. Their practical utility requires capturing realistic environment dynamics and adaptive agent behavior while efficiently simulating million-size populations. Recent advancements in large language models (LLMs) present an opportunity to enhance ABMs by using LLMs as agents with further potential to capture adaptive behavior. However, the computational infeasibility of using LLMs for large populations has hindered their widespread adoption. In this paper, we introduce AgentTorch -- a framework that scales ABMs to millions of agents while capturing high-resolution agent behavior using LLMs. We benchmark the utility of LLMs as ABM agents, exploring the trade-off between simulation scale and individual agency. Using the COVID-19 pandemic as a case study, we demonstrate how AgentTorch can simulate 8.4 million agents representing New York City, capturing the impact of isolation and employment behavior on health and economic outcomes. We compare the performance of different agent architectures based on heuristic and LLM agents in predicting disease waves and unemployment rates. Furthermore, we showcase AgentTorch's capabilities for retrospective, counterfactual, and prospective analyses, highlighting how adaptive agent behavior can help overcome the limitations of historical data in policy design. AgentTorch is an open-source project actively being used for policy-making and scientific discovery around the world. The framework is available here: github.com/AgentTorch/AgentTorch.

Automated text annotation is a compelling use case for generative large language models (LLMs) in social media research. Recent work suggests that LLMs can achieve strong performance on annotation tasks; however, these studies evaluate LLMs on a small number of tasks and likely suffer from contamination due to a reliance on public benchmark datasets. Here, we test a human-centered framework for responsibly evaluating artificial intelligence tools used in automated annotation. We use GPT-4 to replicate 27 annotation tasks across 11 password-protected datasets from recently published computational social science articles in high-impact journals. For each task, we compare GPT-4 annotations against human-annotated ground-truth labels and against annotations from separate supervised classification models fine-tuned on human-generated labels. Although the quality of LLM labels is generally high, we find significant variation in LLM performance across tasks, even within datasets. Our findings underscore the importance of a human-centered workflow and careful evaluation standards: Automated annotations significantly diverge from human judgment in numerous scenarios, despite various optimization strategies such as prompt tuning. Grounding automated annotation in validation labels generated by humans is essential for responsible evaluation.

Differential privacy is widely considered the formal privacy for privacy-preserving data analysis due to its robust and rigorous guarantees, with increasingly broad adoption in public services, academia, and industry. Despite originating in the cryptographic context, in this review paper we argue that, fundamentally, differential privacy can be considered a \textit{pure} statistical concept. By leveraging a theorem due to David Blackwell, our focus is to demonstrate that the definition of differential privacy can be formally motivated from a hypothesis testing perspective, thereby showing that hypothesis testing is not merely convenient but also the right language for reasoning about differential privacy. This insight leads to the definition of $f$-differential privacy, which extends other differential privacy definitions through a representation theorem. We review techniques that render $f$-differential privacy a unified framework for analyzing privacy bounds in data analysis and machine learning. Applications of this differential privacy definition to private deep learning, private convex optimization, shuffled mechanisms, and U.S.~Census data are discussed to highlight the benefits of analyzing privacy bounds under this framework compared to existing alternatives.

Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task. However, existing retrieval-based EAE methods have two main limitations: (1) input length constraints and (2) the gap between the retriever and the inference model. These issues limit the diversity and quality of the retrieved information. In this paper, we propose a Compressive Memory-based Retrieval (CMR) mechanism for EAE, which addresses the two limitations mentioned above. Our compressive memory, designed as a dynamic matrix that effectively caches retrieved information and supports continuous updates, overcomes the limitations of the input length. Additionally, after pre-loading all candidate demonstrations into the compressive memory, the model further retrieves and filters relevant information from memory based on the input query, bridging the gap between the retriever and the inference model. Extensive experiments show that our method achieves new state-of-the-art performance on three public datasets (RAMS, WikiEvents, ACE05), significantly outperforming existing retrieval-based EAE methods.

As AI systems become more advanced, ensuring their alignment with a diverse range of individuals and societal values becomes increasingly critical. But how can we capture fundamental human values and assess the degree to which AI systems align with them? We introduce ValueCompass, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment. We apply ValueCompass to measure the value alignment of humans and language models (LMs) across four real-world vignettes: collaborative writing, education, public sectors, and healthcare. Our findings uncover risky misalignment between humans and LMs, such as LMs agreeing with values like "Choose Own Goals", which are largely disagreed by humans. We also observe values vary across vignettes, underscoring the necessity for context-aware AI alignment strategies. This work provides insights into the design space of human-AI alignment, offering foundations for developing AI that responsibly reflects societal values and ethics.

This paper investigates the task of tracking multiple objects in clutter under a distributed multi-sensor network with time-varying connectivity. Designed with the same objective as the centralised variational multi-object tracker, the proposed method achieves optimal decentralised fusion in performance with local processing and communication with only neighboring sensors. A key innovation is the decentralised construction of a locally maximised evidence lower bound, which greatly reduces the information required for communication. Our decentralised natural gradient descent variational multi-object tracker, enhanced with the gradient tracking strategy and natural gradients that adjusts the direction of traditional gradients to the steepest, shows rapid convergence. Our results verify that the proposed method is empirically equivalent to the centralised fusion in tracking accuracy, surpasses suboptimal fusion techniques with comparable costs, and achieves much lower communication overhead than the consensus-based variational multi-object tracker.

Foundation models pre-trained using self-supervised and weakly-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text generation, and image recognition. Recently, the Earth observation (EO) field has produced several foundation models pre-trained directly on multispectral satellite imagery (e.g., Sentinel-2) for applications like precision agriculture, wildfire and drought monitoring, and natural disaster response. However, few studies have investigated the ability of these models to generalize to new geographic locations, and potential concerns of geospatial bias -- models trained on data-rich developed countries not transferring well to data-scarce developing countries -- remain. We investigate the ability of popular EO foundation models to transfer to new geographic regions in the agricultural domain, where differences in farming practices and class imbalance make transfer learning particularly challenging. We first select six crop classification datasets across five continents, normalizing for dataset size and harmonizing classes to focus on four major cereal grains: maize, soybean, rice, and wheat. We then compare three popular foundation models, pre-trained on SSL4EO-S12, SatlasPretrain, and ImageNet, using in-distribution (ID) and out-of-distribution (OOD) evaluation. Experiments show that pre-trained weights designed explicitly for Sentinel-2, such as SSL4EO-S12, outperform general pre-trained weights like ImageNet. Furthermore, the benefits of pre-training on OOD data are the most significant when only 10--100 ID training samples are used. Transfer learning and pre-training with OOD and limited ID data show promising applications, as many developing regions have scarce crop type labels. All harmonized datasets and experimental code are open-source and available for download.

Comprehensive, precision-laser surveys, conducted via aircraft over the tiny Pacific island of Temwen, have revealed just how advanced its lost city Nan Madol once was. Sometimes called'the Venice of the Pacific,' this megalithic stone city has drawn comparisons to mythic Atlantis -- and even inspired horror writer H.P. Lovecraft, who drew upon news of the site's 1928 discovery as he wrote'The Call of Cthulhu.' But now scores of researchers are in a race to uncover the full extent of Nan Madol's ruins as they undertake plans to preserve the city as a UNESCO World Heritage Site. Their aerial surveys, conducted via LiDAR or'Light Detection and Ranging' laser-mapping, has uncovered'a sophisticated and extensive landscape of cultivation features hidden under Temwen Island's vegetation.' The discovery has promised to rewrite the history of many Pacific Island cultures, showing that societies once presumed to have relied on subsistence fishing and natural tropical bounty, were in fact engaged in sophisticated agricultural planning.