arXiv:2510.20630v1 Announce Type: cross Abstract: This paper explores the application of machine learning (ML) techniques in predicting the QPU processing time of quantum jobs. By leveraging ML algorithms, this study introduces predictive models that are designed to enhance operational efficiency in quantum computing systems. Using a dataset of about 150,000 jobs that follow the IBM […]
Modelling multiscale architecture of biofilm extracellular matrix and its role in oxygen transport
arXiv:2510.19947v1 Announce Type: new Abstract: The extracellular matrix of biofilms presents a dense and intricate architecture. Numerous biophysical properties of the matrix surrounding microbial cells contribute to the heterogeneity of biofilms and their functions at the microscale. Previous mathematical models assume the matrix to be homogeneous, often overlooking the need for a detailed mechanistic understanding […]
Neural Diversity Regularizes Hallucinations in Small Models
arXiv:2510.20690v1 Announce Type: cross Abstract: Language models continue to hallucinate despite increases in parameters, compute, and data. We propose neural diversity — decorrelated parallel representations — as a principled mechanism that reduces hallucination rates at fixed parameter and data budgets. Inspired by portfolio theory, where uncorrelated assets reduce risk by $sqrtP$, we prove hallucination probability […]
MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Cultural Learning
arXiv:2411.12977v5 Announce Type: replace Abstract: Embodied agents powered by large language models (LLMs), such as Voyager, promise open-ended competence in worlds such as Minecraft. However, when powered by open-weight LLMs they still falter on elementary tasks after domain-specific fine-tuning. We propose MindForge, a generative-agent framework for cultural lifelong learning through explicit perspective taking. We introduce […]
Are Stereotypes Leading LLMs’ Zero-Shot Stance Detection ?
arXiv:2510.20154v1 Announce Type: cross Abstract: Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many Natural Language Processing tasks, such as hateful speech detection or sentiment analysis. Surprisingly, the evaluation of this kind of bias in stance detection methods has been largely overlooked by the community. […]
Drug-disease networks and drug repurposing
arXiv:2510.19948v1 Announce Type: new Abstract: Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search […]
PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching
arXiv:2510.20178v1 Announce Type: cross Abstract: Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersion of users. Despite its importance, this task remains challenging due to the difficulty in modeling long-term temporal consistency in a computationally efficient manner. Previous methods attempt to […]
Adaptive Learning in Spatial Agent-Based Models for Climate Risk Assessment: A Geospatial Framework with Evolutionary Economic Agents
arXiv:2509.18633v2 Announce Type: replace Abstract: Climate risk assessment requires modelling complex interactions between spatially heterogeneous hazards and adaptive economic systems. We present a novel geospatial agent-based model that integrates climate hazard data with evolutionary learning for economic agents. Our framework combines Mesa-based spatial modelling with CLIMADA climate impact assessment, introducing adaptive learning behaviours that allow […]
High-order Interactions Modeling for Interpretable Multi-Agent Q-Learning
arXiv:2510.20218v1 Announce Type: cross Abstract: The ability to model interactions among agents is crucial for effective coordination and understanding their cooperation mechanisms in multi-agent reinforcement learning (MARL). However, previous efforts to model high-order interactions have been primarily hindered by the combinatorial explosion or the opaque nature of their black-box network structures. In this paper, we […]
Surfer 2: The Next Generation of Cross-Platform Computer Use Agents
arXiv:2510.19949v1 Announce Type: new Abstract: Building agents that generalize across web, desktop, and mobile environments remains an open challenge, as prior systems rely on environment-specific interfaces that limit cross-platform deployment. We introduce Surfer 2, a unified architecture operating purely from visual observations that achieves state-of-the-art performance across all three environments. Surfer 2 integrates hierarchical context […]