You probably don't give much thought to the device that you're reading this article on, as long as it looks good and keeps working. But the elements that power and run it are the subject of an escalating struggle between the world's two biggest economies - the US and China - with African countries in the eye of the storm. The African continent is rich in critical minerals and metals - like lithium, rare earths, cobalt and tungsten - which are vital to making and running our personal tech. Such materials are also essential for everything from electric vehicles, to AI data centres, and weapon systems. China has long been the biggest player in the global market for critical minerals and metals.

Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the level of an expert. We construct an expert-curated database of 1,726 scientific papers that covers the history of the field, and a set of 67 expert-formulated questions that probe deep understanding of the literature. We then evaluate six different LLM-based systems for answering these questions, including both commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. Experts then evaluate the answers of these systems against a rubric that assesses balanced perspectives, factual comprehensiveness, succinctness, and evidentiary support. Among the six systems two using RAG on curated literature outperformed existing closed models across key metrics, particularly in providing comprehensive and well-supported answers. We discuss promising aspects of LLM performances as well as critical short-comings of all the models. The set of expert-formulated questions and the rubric will be valuable for assessing expert level performance of LLM based reasoning systems.

Retrieval-augmented generation (RAG) aims to reduce hallucinations by grounding responses in external context, yet large language models (LLMs) still frequently introduce unsupported information or contradictions even when provided with relevant context. This paper presents two complementary efforts at Vectara to measure and benchmark LLM faithfulness in RAG. First, we describe our original hallucination leaderboard, which has tracked hallucination rates for LLMs since 2023 using our HHEM hallucination detection model. Motivated by limitations observed in current hallucination detection methods, we introduce FaithJudge, an LLM-as-a-judge framework that leverages a pool of diverse human-annotated hallucination examples to substantially improve the automated hallucination evaluation of LLMs. We introduce an enhanced hallucination leaderboard centered on FaithJudge that benchmarks LLMs on RAG faithfulness in summarization, question-answering, and data-to-text generation tasks. FaithJudge enables a more reliable benchmarking of LLM hallucinations in RAG and supports the development of more trustworthy generative AI systems: https://github.com/vectara/FaithJudge.

The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one-dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics-based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications.

Large language models (LLMs) exhibit strong semantic understanding, yet struggle when user instructions involve ambiguous or conceptually misaligned terms. We propose the Language Graph Model (LGM) to enhance conceptual clarity by extracting meta-relations-inheritance, alias, and composition-from natural language. The model further employs a reflection mechanism to validate these meta-relations. Leveraging a Concept Iterative Retrieval Algorithm, these relations and related descriptions are dynamically supplied to the LLM, improving its ability to interpret concepts and generate accurate responses. Unlike conventional Retrieval-Augmented Generation (RAG) approaches that rely on extended context windows, our method enables large language models to process texts of any length without the need for truncation. Experiments on standard benchmarks demonstrate that the LGM consistently outperforms existing RAG baselines.

Designing materials with targeted properties remain s challenging due to the vastness of chemical space and the scarcity of propert y-labeled data. While r ecent advances in generative models offer a promising w ay for inverse design, most approaches require large datasets and must be retrained for every new target property. Here, we introduce the EGMOF ( Efficient Generation of MOFs), a hybrid diffusion-transformer framework that overcome s these limitations through a modular, descriptor - mediated workflow. EGMOF decomposes inverse design into two steps: (1) a one -dimensional diffusion model (Prop2Desc) that maps desired properties to chemically meaningful descriptors followed by (2) a transformer model (Desc2MOF) that generates structures from the se descriptors. This modular hybrid design enables minimal retraining and maintains high accuracy even under small-data conditions. On a hydrogen uptake dataset, EGMOF achieved over 95 % validity and 84% hit rate, representing significant improvements of up to 57 % in validity and 14% in hit rate compared to existing methods, while remaining effective with only 1,000 training samples . Moreover, our model successfully performed conditional generation across 29 diverse property datasets, including CoREMOF, QMOF, and text - mined experimental datasets, whereas previous models have not. This work presents a data - efficient, generalizable approach to the inverse design of diverse MOFs and highlights the potential of modular inverse design workflows for broader materials discovery.

Sequential deep learning models excel in domains with temporal or sequential dependencies, but their complexity necessitates post-hoc feature attribution methods for understanding their predictions. While existing techniques quantify feature importance, they inherently assume fixed feature ordering - conflating the effects of (1) feature values and (2) their positions within input sequences. To address this gap, we introduce OrdShap, a novel attribution method that disentangles these effects by quantifying how a model's predictions change in response to permuting feature position. We establish a game-theoretic connection between OrdShap and Sanchez-Bergantiños values, providing a theoretically grounded approach to position-sensitive attribution. Empirical results from health, natural language, and synthetic datasets highlight OrdShap's effectiveness in capturing feature value and feature position attributions, and provide deeper insight into model behavior.

Elucidating reaction mechanisms hinges on efficiently generating transition states (TSs), products, and complete reaction networks. Recent generative models, such as diffusion models for TS sampling and sequence-based architectures for product generation, offer faster alternatives to quantum-chemistry searches. But diffusion models remain constrained by their stochastic differential equation (SDE) dynamics, which suffer from inefficiency and limited controllability. We show that flow matching, a deterministic ordinary differential (ODE) formulation, can replace SDE-based diffusion for molecular and reaction generation. We introduce MolGEN, a conditional flow-matching framework that learns an optimal transport path to transport Gaussian priors to target chemical distributions. On benchmarks used by TSDiff and OA-ReactDiff, MolGEN surpasses TS geometry accuracy and barrier-height prediction while reducing sampling to sub-second inference. MolGEN also supports open-ended product generation with competitive top-k accuracy and avoids mass/electron-balance violations common to sequence models. In a realistic test on the $γ$-ketohydroperoxide decomposition network, MolGEN yields higher fractions of valid and intended TSs with markedly fewer quantum-chemistry evaluations than string-based baselines. These results demonstrate that deterministic flow matching provides a unified, accurate, and computationally efficient foundation for molecular generative modeling, signaling that flow matching is the future for molecular generation across chemistry.

New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' Driver screaming'Allahu Akbar' ploughs in to pedestrians'trying to hit everyone he encountered' on French holiday island leaving ten injured This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Amazon signals it's finally fed up with Whole Foods' sluggish sales - and is making sweeping, controversial changes Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese Now he's dead, here's the full story of what happened that day... and the ghastly aftermath no one knows about Shocking moment Mexico's president is groped by man who grabs her breasts and tries to kiss her Miss Universe contestant called'dumb' in humiliating dressing-down by official hits back with powerful speech as furious organisers condemn her treatment and he issues grovelling apology Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Nepo baby turns heads at Glamour Women Of The Year Awards in a glitzy gold sequin feathered gown - but can YOU guess who her A-list mother is? New footage reveals the moments before football manager collapsed and died mid-match, leaving his players in disbelief, as it emerges he'complained about fish he had eaten' hours before Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Scientists have been faced with a huge dilemma, as research reveals that reducing air pollution has increased global warming . While smog kills millions of people every year, it also whitens clouds - making them more reflective. So by slashing air pollution, we're inadvertently diminishing the brightness of clouds, which are key regulators of global temperature.

This study investigates the application of Random Forest Regression for predicting mechanical properties of alloy steel-Elongation, Tensile Strength, and Yield Strength-from material composition features including Iron (Fe), Chromium (Cr), Nickel (Ni), Manganese (Mn), Silicon (Si), Copper (Cu), Carbon (C), and deformation percentage during cold rolling. Utilizing a dataset comprising these features, we trained and evaluated the Random Forest model, achieving high predictive performance as evidenced by R2 scores and Mean Squared Errors (MSE). The results demonstrate the model's efficacy in providing accurate predictions, which is validated through various performance metrics including residual plots and learning curves. The findings underscore the potential of ensemble learning techniques in enhancing material property predictions, with implications for industrial applications in material science.