Remote-sensing mineral exploration is critical for identifying economically viable mineral deposits, yet it poses significant challenges for multimodal large language models (MLLMs). These include limitations in domain-specific geological knowledge and difficulties in reasoning across multiple remote-sensing images, further exacerbating long-context issues. To address these, we present MineAgent, a modular framework leveraging hierarchical judging and decision-making modules to improve multi-image reasoning and spatial-spectral integration. Complementing this, we propose MineBench, a benchmark specific for evaluating MLLMs in domain-specific mineral exploration tasks using geological and hyperspectral data. Extensive experiments demonstrate the effectiveness of MineAgent, highlighting its potential to advance MLLMs in remote-sensing mineral exploration.

In this study, we immerse in the intricate world of patterns, examining the structural details of Indian medieval architecture for the discovery of motifs with great application potential from the mechanical metastructure perspective. The motifs that specifically engrossed us are derived from the tomb of I'timad-ud-Daula, situated in the city of Agra, close to the Taj Mahal. In an exploratory study, we designed nine interlaced metastructures inspired from the tomb's motifs. We fabricated the metastructures using additive manufacturing and studied their vibration characteristics experimentally and numerically. We also investigated bandgap modulation with metallic inserts in honeycomb interlaced metastructures. The comprehensive study of these metastructure panels reveals their high performance in controlling elastic wave propagation and generating suitable frequency bandgaps, hence having potential applications as waveguides for noise and vibration control. Finally, we developed a novel AI-based model trained on numerical datasets for the inverse design of metastructures with a desired bandgap.

Electroadhesion is an electrically controllable switchable adhesive commonly used in soft robots and haptic user interfaces. It can form strong bonds to a wide variety of surfaces at low power consumption. However, electroadhesive clutches in the literature engage to and release from substrates several orders of magnitude slower than a traditional electrostatic model would predict, limiting their usefulness in high-bandwidth applications. We develop a novel electromechanical model for electroadhesion, factoring in polarization dynamics and contact mechanics between the dielectric and substrate. We show in simulation and experimentally how different design parameters affect the engagement and release times of electroadhesive clutches to metallic substrates. In particular, we find that higher drive frequencies and narrower substrate aspect ratios enable significantly faster dynamics. We demonstrate designs with engagement times under 15 us and release times as low as 875 us, which are 10x and 17.1x faster, respectively, than the best times found in prior literature.

Air pollution, particularly airborne particulate matter (PM), poses a significant threat to public health globally. It is crucial to comprehend the association between PM-associated toxic components and their cellular targets in humans to understand the mechanisms by which air pollution impacts health and to establish causal relationships between air pollution and public health consequences. Current methods for modeling and analyzing these interactions are rudimentary, with experimental approaches offering limited throughput and comprehensiveness. Leveraging cutting-edge deep learning technologies, we developed tipFormer (toxin-protein interaction prediction based on transformer), a novel machine-learning approach for identifying toxic components capable of penetrating human cells and instigating pathogenic biological activities and signaling cascades. It incorporates dual pre-trained language models to derive encodings for protein sequences and chemicals. It employs a convolutional encoder to assimilate the sequential attributes of proteins and chemicals. It then introduces a novel learning module with a cross-attention mechanism to decode and elucidate the multifaceted interactions pivotal for the hotspots binding proteins and chemicals. Through thorough experimentation, tipFormer was shown to be proficient in capturing interactions between proteins and toxic components. This approach offers significant value to the air quality and toxicology research communities by enabling high-throughput, high-content identification and prioritization of hazards. Keywords: Air pollution, toxin-protein interaction, computational modeling, attention mechanisms 1. Introduction Air pollution has emerged as a critical global health concern, primarily driven by rapid economic, industrial and population growth and further exacerbated by climate change and other non-anthropogenic factors [1]. The World Health Organization estimates that approximately 7 million premature deaths occur every year due to air pollution exposure. The consequences of air pollution extend far beyond individual health implications and exacerbate the strain on societal and healthcare systems in numerous ways [2]. The health risks associated with airborne particulate matter (PM) are particularly concerning for public health [3].

We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments. We establish human baselines for each competition using Kaggle's publicly available leaderboards. We use open-source agent scaffolds to evaluate several frontier language models on our benchmark, finding that the best-performing setup--OpenAI's o1-preview with AIDE scaffolding--achieves at least the level of a Kaggle bronze medal in 16.9% of competitions. In addition to our main results, we investigate various forms of resource scaling for AI agents and the impact of contamination from pre-training. We open-source our benchmark code (github.com/openai/mle-bench/) to facilitate future research in understanding the ML engineering capabilities of AI agents.

Enterprises possess a vast array of API assets scattered across various functions, forming the backbone of existing business processes. By leveraging these APIs as functional tools, enterprises can design diverse, scenario-specific agent applications, driven by on-premise function-calling models as the core engine. However, generic models often fail to meet enterprise requirements in terms of computational efficiency, output accuracy, and stability, necessitating scenario-specific adaptation. In this paper, we propose a training pipeline for function-calling capabilities tailored to real-world business scenarios. This pipeline includes the synthesis and augmentation of scenario-specific function-calling data, model fine-tuning, and performance evaluation and analysis. Using this pipeline, we generated 1,260 fully AI-generated samples and 1,035 augmented manually-labeled samples in digital HR agent scenario. The Qwen2.5-Coder-7B-Instruct model was employed as the base model and fine-tuned using the LoRA method on four GPUs with 24GB VRAM. Our fine-tuned model demonstrated outstanding performance in evaluations and practical applications, surpassing GPT-4 and GPT-4o in accuracy on the test set. These results validate the reliability of the proposed pipeline for training scenario-specific function-calling models.

The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resource-demanding process. To accelerate the design of novel metamaterials, we present MetaScientist, a human-in-the-loop system that integrates advanced AI capabilities with expert oversight with two primary phases: (1) hypothesis generation, where the system performs complex reasoning to generate novel and scientifically sound hypotheses, supported with domain-specific foundation models and inductive biases retrieved from existing literature; (2) 3D structure synthesis, where a 3D structure is synthesized with a novel 3D diffusion model based on the textual hypothesis and refined it with a LLM-based refinement model to achieve better structure properties. At each phase, domain experts iteratively validate the system outputs, and provide feedback and supplementary materials to ensure the alignment of the outputs with scientific principles and human preferences. Through extensive evaluation from human scientists, MetaScientist is able to deliver novel and valid mechanical metamaterial designs that have the potential to be highly impactful in the metamaterial field.

The integration of Large Vision-Language Models (LVLMs) such as OpenAI's GPT-4 Vision into various sectors has marked a significant evolution in the field of artificial intelligence, particularly in the analysis and interpretation of visual data. This paper explores the practical application of GPT-4 Vision in the construction industry, focusing on its capabilities in monitoring and tracking the progress of construction projects. Utilizing high-resolution aerial imagery of construction sites, the study examines how GPT-4 Vision performs detailed scene analysis and tracks developmental changes over time. The findings demonstrate that while GPT-4 Vision is proficient in identifying construction stages, materials, and machinery, it faces challenges with precise object localization and segmentation. Despite these limitations, the potential for future advancements in this technology is considerable. This research not only highlights the current state and opportunities of using LVLMs in construction but also discusses future directions for enhancing the model's utility through domain-specific training and integration with other computer vision techniques and digital twins.

Artificial intelligence can tell Scotch whisky from American whiskey and identify its strongest constituent aromas more reliably than human experts – by using data rather than tasting the drinks. Andreas Grasskamp at the Fraunhofer Institute for Process Engineering and Packaging IVV in Germany and his colleagues trained an AI molecular odour prediction algorithm called OWSum on descriptions of different whiskies. Then, in a study involving 16 samples – nine types of Scotch whisky and seven types of American bourbon or whiskey – they tasked OWSum with telling drinks from the two nations apart based on keyword descriptions of their flavours, such as flowery, fruity, woody or smoky. Using these alone, the AI could tell which country a drink came from with almost 94 per cent accuracy. Because the complex aroma of these spirits is determined by the absence or presence of many chemical compounds, the researchers also fed the AI a reference dataset of 390 molecules commonly found in whiskies.

Andrews Ata Kangah is a team leader and researcher working on democratizing AI and AI solutions for environmental problems. We spoke to him about his research, attending the AfriClimate AI workshop at the Deep Learning Indaba, and what inspired him to work in AI and on climate-related projects. My name is Andrews Ata Kangah. I also double as a researcher at Armtos, which is a non-profit. At Armtos, our current goal is to build a solution to solve the illegal mining problem that's going on in Ghana. The mining is destroying the lands that are within mining areas.