New biodiversity and ecosystem reporting frameworks require companies to collect data on multifaceted impacts on complex ecological systems over space and time while offering them limited guidance on how to do so. Artificial Intelligence (AI) and Earth Observation (EO) are powerful tools that can help…
Purpose General-domain large language models (LLMs) have emerged as valuable tools in healthcare, however, their ability to understand and perform tasks based on data stored in tabular form has not been explored in Ophthalmology. We aimed to assess OpenAI’s Generative Pre-trained Transformer 4o (GPT-4o) performance…
Virtual 3D meetings offer the potential to enhance copresence, increase engagement and thus improve effectiveness of remote meetings compared to standard 2D video calls. However, representing people in 3D meetings remains a challenge; existing solutions achieve high quality by using complex hardware, making use of…
Reliable autoformalization remains an elusive goal even in the era of large language models (LLMs). Even the best LLMs struggle to translate natural language into formal constructs in languages like Lean. High-quality data for formal theorem proving has been a key bottleneck given the resource…
Despite significant advances in Multimodal Large Language Models (MLLMs), understanding complex temporal dynamics in videos remains a major challenge. Our experiments show that current Video Large Language Model (Video-LLM) architectures have critical limitations in temporal understanding, struggling with tasks that require detailed comprehension of action…
Reconstruction of 3D scenes from a single image is a crucial step towards enabling next-generation AI-powered immersive experiences. However, existing diffusion-based methods often struggle with reconstructing omnidirectional scenes due to geometric distortions and inconsistencies across the generated novel views, hindering accurate 3D recovery. To overcome…
Metal-Organic Frameworks (MOFs) have attracted considerable attention due to their unique properties including high surface area and tunable porosity, and promising applications in catalysis, gas storage, and drug delivery. Structure prediction for MOFs is a challenging task, as these frameworks are intrinsically periodic and hierarchically…
Nature-based Solutions (NbS) encompass a spectrum of conservation and restoration actions aimed at improving biodiversity, climate, and/or water outcomes. Considerable research exists that focuses either on conservation or restoration, or on one particular environmental outcome. Yet, there is a need to develop integrated frameworks that…
The increasing number of microphone-equipped personal devices offers great flexibility and potential using them as ad-hoc microphone arrays in dynamic meeting environments. However, most existing approaches are designed for time-synchronized microphone setups, a condition that may not hold in real-world meeting scenarios, where time latency…
As large language models (LLMs) are increasingly used for code generation, concerns over the security risks have grown substantially. Early research has primarily focused on red teaming, which aims to uncover and evaluate vulnerabilities and risks of CodeGen models. However, progress on the blue teaming…
Finetuning large language models (LLMs) is a resource-intensive task for researchers in academia, with memory constraints posing a key bottleneck. A classic optimization method, block coordinate descent (BCD), significantly reduces memory cost by segmenting the trainable parameters into multiple blocks and optimizing one active block…
Reasoning‐focused large language models (LLMs) sometimes alter their behavior when they detect that they are being evaluated—an effect analogous to the Hawthorne phenomenon—which can lead them to optimize for test‐passing performance or to comply more readily with harmful prompts if real‐world consequences appear absent. We…
