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Machine unlearning techniques aim to mitigate unintended memorization in large language models (LLMs). However, existing approaches predominantly focus on the explicit removal of isolated facts, often overlooking latent inferential dependencies and the non-deterministic nature of knowledge within LLMs. Consequently, facts presumed forgotten may persist implicitly through correlated information. To address these challenges, we propose a knowledge unlearning evaluation framework that more accurately captures the implicit structure of real-world knowledge by representing relevant factual contexts as knowledge graphs with associated confidence scores. We further develop an inference-based evaluation protocol leveraging powerful LLMs as judges; these judges reason over the extracted knowledge subgraph to determine unlearning success. Our LLM judges utilize carefully designed prompts and are calibrated against human evaluations to ensure their trustworthiness and stability. Extensive experiments on our newly constructed benchmark demonstrate that our framework provides a more realistic and rigorous assessment of unlearning performance. Moreover, our findings reveal that current evaluation strategies tend to overestimate unlearning effectiveness.

Pope Leo XIV speaks during a meeting with bishops, members of the clergy and families whose members have been victims of environmental pollution at the Cathedral of Santa Maria Assunta, in Acerra, Italy, on 23 May 2026. Pope Leo XIV speaks during a meeting with bishops, members of the clergy and families whose members have been victims of environmental pollution at the Cathedral of Santa Maria Assunta, in Acerra, Italy, on 23 May 2026. Americans echo Pope Leo's concerns about AI: 'It threatens workers, privacy and human life' Guardian readers in the US spoke of fears about unregulated AI in response to the pope's encyclical warning about the risks of the technology I n his first major papal text since assuming leadership of the Catholic church last year, Pope Leo issued a stark warning about the rise of artificial intelligence this week, denouncing the "culture of power" driving the AI age. Calling for the "most rigorous" ethical constraints on AI - which he described as one of the greatest threats facing humanity today - the first US-born pope also warned of "new forms of slavery" emerging through the digital economy. Speaking to the Guardian, readers in the US echoed the pope's concerns, describing AI as an "unregulated" industry increasingly being used to the "detriment of too many people", while also raising fears about surveillance, labor displacement, war and environmental harm .

Papa Johns launches drone delivery testing with Wing in Indian Trail, North Carolina, starting with sandwiches while custom pizza packaging is developed for future flights.

A new collaboration with Alphabet's Wing will only deliver sandwiches. It demonstrates the tricky parts of taking to the sky. Starting today, eager customers of the US pizza restaurant chain Papa Johns living in one corner of southern North Carolina will have the opportunity to receive their food from the sky, thanks to a new collaboration with Alphabet's drone company, Wing . But Papa Johns' signature pizzas won't be on offer. Instead, drone-loving North Carolinians will have to choose between three kinds of sandwiches, a newer product for the fast-food chain: Philly cheesesteak, chicken bacon ranch, or steak and mushroom varieties.

A nonparametric model using a sequence of Bernstein polynomials is constructed to approximate arbitrary isotropic covariance functions valid in $\mathbb{R}^\infty$ and related approximation properties are investigated using the popular $L_{\infty}$ norm and $L_2$ norms. A computationally efficient sieve maximum likelihood (sML) estimation is then developed to nonparametrically estimate the unknown isotropic covaraince function valid in $\mathbb{R}^\infty$. Consistency of the proposed sieve ML estimator is established under increasing domain regime. The proposed methodology is compared numerically with couple of existing nonparametric as well as with commonly used parametric methods. Numerical results based on simulated data show that our approach outperforms the parametric methods in reducing bias due to model misspecification and also the nonparametric methods in terms of having significantly lower values of expected $L_{\infty}$ and $L_2$ norms. Application to precipitation data is illustrated to showcase a real case study. Additional technical details and numerical illustrations are also made available.

Off-policy evaluation (OPE) is important for closing the gap between offline training and evaluation of reinforcement learning (RL), by estimating performance and/or rank of target (evaluation) policies using offline trajectories only. It can improve the safety and efficiency of data collection and policy testing procedures in situations where online deployments are expensive, such as healthcare. However, existing OPE methods fall short in estimating human feedback (HF) signals, as HF may be conditioned over multiple underlying factors and is only sparsely available; as opposed to the agent-defined environmental rewards (used in policy optimization), which are usually determined over parametric functions or distributions. Consequently, the nature of HF signals makes extrapolating accurate OPE estimations to be challenging. To resolve this, we introduce an OPE for HF (OPEHF) framework that revives existing OPE methods in order to accurately evaluate the HF signals. Specifically, we develop an immediate human reward (IHR) reconstruction approach, regularized by environmental knowledge distilled in a latent space that captures the underlying dynamics of state transitions as well as issuing HF signals. Our approach has been tested over two real-world experiments, adaptive in-vivo neurostimulation and intelligent tutoring, as well as in a simulation environment (visual Q&A). Results show that our approach significantly improves the performance toward estimating HF signals accurately, compared to directly applying (variants of) existing OPE methods.

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