Agentic UAVs represent a new frontier in autonomous aerial intelligence, integrating perception, decision-making, memory, and collaborative planning to operate adaptively in complex, real-world environments. Driven by recent advances in Agentic AI, these systems surpass traditional UAVs by exhibiting goal-driven behavior, contextual reasoning, and interactive autonomy. We provide a comprehensive foundation for understanding the architectural components and enabling technologies that distinguish Agentic UAVs from traditional autonomous UAVs. Furthermore, a detailed comparative analysis highlights advancements in autonomy with AI agents, learning, and mission flexibility. This study explores seven high-impact application domains precision agriculture, construction & mining, disaster response, environmental monitoring, infrastructure inspection, logistics, security, and wildlife conservation, illustrating the broad societal value of agentic aerial intelligence. Furthermore, we identify key challenges in technical constraints, regulatory limitations, and data-model reliability, and we present emerging solutions across hardware innovation, learning architectures, and human-AI interaction. Finally, a future roadmap is proposed, outlining pathways toward self-evolving aerial ecosystems, system-level collaboration, and sustainable, equitable deployments. This survey establishes a foundational framework for the future development, deployment, and governance of agentic aerial systems (Agentic UAVs) across diverse societal and industrial domains.

To meet the demands of content moderation, online platforms have resorted to automated systems. Newer forms of real-time engagement($\textit{e.g.}$, users commenting on live streams) on platforms like Twitch exert additional pressures on the latency expected of such moderation systems. Despite their prevalence, relatively little is known about the effectiveness of these systems. In this paper, we conduct an audit of Twitch's automated moderation tool ($\texttt{AutoMod}$) to investigate its effectiveness in flagging hateful content. For our audit, we create streaming accounts to act as siloed test beds, and interface with the live chat using Twitch's APIs to send over $107,000$ comments collated from $4$ datasets. We measure $\texttt{AutoMod}$'s accuracy in flagging blatantly hateful content containing misogyny, racism, ableism and homophobia. Our experiments reveal that a large fraction of hateful messages, up to $94\%$ on some datasets, $\textit{bypass moderation}$. Contextual addition of slurs to these messages results in $100\%$ removal, revealing $\texttt{AutoMod}$'s reliance on slurs as a moderation signal. We also find that contrary to Twitch's community guidelines, $\texttt{AutoMod}$ blocks up to $89.5\%$ of benign examples that use sensitive words in pedagogical or empowering contexts. Overall, our audit points to large gaps in $\texttt{AutoMod}$'s capabilities and underscores the importance for such systems to understand context effectively.

Modeling urban crime is an important yet challenging task that requires understanding the subtle visual, social, and cultural cues embedded in urban environments. Previous work has mainly focused on rule-based agent-based modeling (ABM) and deep learning methods. ABMs offer interpretability of internal mechanisms but exhibit limited predictive accuracy. In contrast, deep learning methods are often effective in prediction but are less interpretable and require extensive training data. Moreover, both lines of work lack the cognitive flexibility to adapt to changing environments. Leveraging the capabilities of large language models (LLMs), we propose CrimeMind, a novel LLM-driven ABM framework for simulating urban crime within a multi-modal urban context. A key innovation of our design is the integration of the Routine Activity Theory (RAT) into the agentic workflow of CrimeMind, enabling it to process rich multi-modal urban features and reason about criminal behavior. However, RAT requires LLM agents to infer subtle cues in evaluating environmental safety as part of assessing guardianship, which can be challenging for LLMs. To address this, we collect a small-scale human-annotated dataset and align CrimeMind's perception with human judgment via a training-free textual gradient method. Experiments across four major U.S. cities demonstrate that CrimeMind outperforms both traditional ABMs and deep learning baselines in crime hotspot prediction and spatial distribution accuracy, achieving up to a 24% improvement over the strongest baseline. Furthermore, we conduct counterfactual simulations of external incidents and policy interventions and it successfully captures the expected changes in crime patterns, demonstrating its ability to reflect counterfactual scenarios. Overall, CrimeMind enables fine-grained modeling of individual behaviors and facilitates evaluation of real-world interventions.

Identifying bias in LLM-generated content is a crucial prerequisite for ensuring fairness in LLMs. Existing methods, such as fairness classifiers and LLM-based judges, face limitations related to difficulties in understanding underlying intentions and the lack of criteria for fairness judgment. In this paper, we introduce BiasGuard, a novel bias detection tool that explicitly analyzes inputs and reasons through fairness specifications to provide accurate judgments. BiasGuard is implemented through a two-stage approach: the first stage initializes the model to explicitly reason based on fairness specifications, while the second stage leverages reinforcement learning to enhance its reasoning and judgment capabilities. Our experiments, conducted across five datasets, demonstrate that BiasGuard outperforms existing tools, improving accuracy and reducing over-fairness misjudgments. We also highlight the importance of reasoning-enhanced decision-making and provide evidence for the effectiveness of our two-stage optimization pipeline.

A 10-year moratorium on state-level AI regulation included in President Donald Trump's "Big Beautiful Bill" has brushed up against a mounting battle over the growth of data centers. On Thursday, Representative Thomas Massie, a Kentucky Republican, posted on X that the megabill's 10-year block on states regulating artificial intelligence could "make it easier for corporations to get zoning variances, so massive AI data centers could be built in close proximity to residential areas." Massie, who did not vote for the bill, followed up his initial tweet with a screenshot of a story on a proposed data center in Oldham County, Kentucky, which downsized and changed locations following local pushback. "This isn't a conspiracy theory; this was a recent issue in my Congressional district," he wrote of concerns over the placement of data centers. "It was resolved at the local level because local officials had leverage. The big beautiful bill undermines the ability of local communities to decide where the AI data centers will be built."

Authors and publishers have filed multiple lawsuits over this issue, and in a new twist, researchers have shown that at least one AI model has not only used popular books in its training data, but also memorised their contents verbatim. But now, researchers have tested multiple models to see how much of that training data they can spit back out verbatim. They found that many models do not retain the exact text of the books in their training data – but one of Meta's models has memorised almost the entirety of certain books. If judges rule against the company, the researchers estimate that this could make Meta liable for at least 1 billion in damages. "That means, on the one hand, that AI models are not just'plagiarism machines', as some have alleged, but it also means that they do more than just learn general relationships between words," says Mark Lemley at Stanford University in California.

Elon Musk and Donald Trump are no longer friends. Tension between the two exploded into public view in the middle of last week, with each leveling sharp barbs at the other. Four days into the public feud between the world's most powerful person and the world's richest person, though, I declare Musk the loser. An unstoppable force has lost its battle with an immovable object. From my colleagues Hugo Lowell and Andrew Roth: On Thursday, Elon Musk called for Donald Trump's impeachment and mocked his connections to the convicted sex offender Jeffrey Epstein, as the US president threatened to cancel federal contracts and tax subsidies for Musk's companies, in an extraordinary social media feud that erupted between the former allies.

This paper introduces the Difference-in-Differences Bayesian Causal Forest (DiD-BCF), a novel non-parametric model addressing key challenges in DiD estimation, such as staggered adoption and heterogeneous treatment effects. DiD-BCF provides a unified framework for estimating Average (ATE), Group-Average (GATE), and Conditional Average Treatment Effects (CATE). A core innovation, its Parallel Trends Assumption (PTA)-based reparameterization, enhances estimation accuracy and stability in complex panel data settings. Extensive simulations demonstrate DiD-BCF's superior performance over established benchmarks, particularly under non-linearity, selection biases, and effect heterogeneity. Applied to U.S. minimum wage policy, the model uncovers significant conditional treatment effect heterogeneity related to county population, insights obscured by traditional methods. DiD-BCF offers a robust and versatile tool for more nuanced causal inference in modern DiD applications.

With the growing adoption of data privacy regulations, the ability to erase private or copyrighted information from trained models has become a crucial requirement. Traditional unlearning methods often assume access to the complete training dataset, which is unrealistic in scenarios where the source data is no longer available. To address this challenge, we propose a certified unlearning framework that enables effective data removal \final{without access to the original training data samples}. Our approach utilizes a surrogate dataset that approximates the statistical properties of the source data, allowing for controlled noise scaling based on the statistical distance between the two. \updated{While our theoretical guarantees assume knowledge of the exact statistical distance, practical implementations typically approximate this distance, resulting in potentially weaker but still meaningful privacy guarantees.} This ensures strong guarantees on the model's behavior post-unlearning while maintaining its overall utility. We establish theoretical bounds, introduce practical noise calibration techniques, and validate our method through extensive experiments on both synthetic and real-world datasets. The results demonstrate the effectiveness and reliability of our approach in privacy-sensitive settings.

Large Language Models (LLMs) require alignment with human preferences to avoid generating offensive, false, or meaningless content. Recently, low-resource methods for LLM alignment have been popular, while still facing challenges in obtaining both high-quality and aligned content. Motivated by the observation that the difficulty of generating aligned responses is concentrated at the beginning of decoding, we propose a novel framework, Weak-to-Strong Decoding (WSD), to enhance the alignment ability of base models by the guidance of a small aligned model. The small model first drafts well-aligned beginnings, followed by the large base model to continue the rest, controlled by a well-designed auto-switch mechanism. We also collect a new dataset, GenerAlign, to fine-tune a small-sized Pilot-3B as the draft model, which effectively enhances different base models under the WSD framework to outperform all baseline methods, while avoiding degradation on downstream tasks, termed as the alignment tax. Extensive experiments are further conducted to examine the impact of different settings and time efficiency, as well as analyses on the intrinsic mechanisms of WSD in depth.