arXiv:2511.03641v1 Announce Type: cross Abstract: To foster trustworthy Artificial Intelligence (AI) within the European Union, the AI Act requires providers to mark and detect the outputs of their general-purpose models. The Article 50 and Recital 133 call for marking methods that are ”sufficiently reliable, interoperable, effective and robust”. Yet, the rapidly evolving and heterogeneous landscape […]
From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers
arXiv:2511.03235v1 Announce Type: new Abstract: Psychological constructs within individuals are widely believed to be interconnected. We investigated whether and how Large Language Models (LLMs) can model the correlational structure of human psychological traits from minimal quantitative inputs. We prompted various LLMs with Big Five Personality Scale responses from 816 human individuals to role-play their responses […]
Whisper Leak: a side-channel attack on Large Language Models
arXiv:2511.03675v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications, where privacy is paramount. This paper introduces Whisper Leak, a side-channel attack that infers user prompt topics from encrypted LLM traffic by analyzing packet size and timing patterns in streaming responses. Despite TLS […]
A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision
arXiv:2503.06286v3 Announce Type: replace Abstract: Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are boosting computational neuroscience research by enabling models of the brain with performances beyond what was possible just a decade ago. However, because the stimuli of these datasets typically live within a common naturalistic visual distribution, they do not […]
Reading Between the Lines: The One-Sided Conversation Problem
arXiv:2511.03056v1 Announce Type: cross Abstract: Conversational AI is constrained in many real-world settings where only one side of a dialogue can be recorded, such as telemedicine, call centers, and smart glasses. We formalize this as the one-sided conversation problem (1SC): inferring and learning from one side of a conversation. We study two tasks: (1) reconstructing […]
Topography, climate, land cover, and biodiversity: Explaining endemic richness and management implications on a Mediterranean island
arXiv:2511.03242v1 Announce Type: new Abstract: Island endemism is shaped by complex interactions among environmental, ecological, and evolutionary factors, yet the relative contributions of topography, climate, and land cover remain incompletely quantified. We investigated the drivers of endemic plant richness across Crete, a Mediterranean biodiversity hotspot, using spatially explicit data on species distributions, topographic complexity, climatic […]
Scaling Multi-Agent Environment Co-Design with Diffusion Models
arXiv:2511.03100v1 Announce Type: cross Abstract: The agent-environment co-design paradigm jointly optimises agent policies and environment configurations in search of improved system performance. With application domains ranging from warehouse logistics to windfarm management, co-design promises to fundamentally change how we deploy multi-agent systems. However, current co-design methods struggle to scale. They collapse under high-dimensional environment design […]
Modelling the interaction between ethnicity and infectious disease transmission dynamics in Aotearoa New Zealand during the first Omicron wave of the COVID-19 pandemic
arXiv:2507.10925v2 Announce Type: replace Abstract: During the COVID-19 pandemic, Aotearoa followed an elimination strategy followed by a mitigation strategy, which saw high success and kept health impact low. However, there were inequities in health outcomes, notably that M=aori and Pacific Peoples had lower vaccine coverage and experienced higher age-standardised rates of hospitalisation and death. Models […]
Adaptive Detection of Software Aging under Workload Shift
arXiv:2511.03103v1 Announce Type: cross Abstract: Software aging is a phenomenon that affects long-running systems, leading to progressive performance degradation and increasing the risk of failures. To mitigate this problem, this work proposes an adaptive approach based on machine learning for software aging detection in environments subject to dynamic workload conditions. We evaluate and compare a […]
FAPEX: Fractional Amplitude-Phase Expressor for Robust Cross-Subject Seizure Prediction
arXiv:2511.03263v1 Announce Type: new Abstract: Precise, generalizable subject-agnostic seizure prediction (SASP) remains a fundamental challenge due to the intrinsic complexity and significant spectral variability of electrophysiological signals across individuals and recording modalities. We propose FAPEX, a novel architecture that introduces a learnable fractional neural frame operator (FrNFO) for adaptive time-frequency decomposition. Unlike conventional models that […]