arXiv:2511.18723v4 Announce Type: replace Abstract: Parallelization has emerged as a promising approach for accelerating MILP solving. However, the complexity of the branch-and-bound (B&B) framework and the numerous effective algorithm components in MILP solvers make it difficult to parallelize. In this study, a scalable parallel framework, N2N (a node-to-node framework that maps the B&B nodes to […]
Protecting Deep Neural Network Intellectual Property with Chaos-Based White-Box Watermarking
arXiv:2512.16658v1 Announce Type: cross Abstract: The rapid proliferation of deep neural networks (DNNs) across several domains has led to increasing concerns regarding intellectual property (IP) protection and model misuse. Trained DNNs represent valuable assets, often developed through significant investments. However, the ease with which models can be copied, redistributed, or repurposed highlights the urgent need […]
Meta-RL Induces Exploration in Language Agents
arXiv:2512.16848v1 Announce Type: cross Abstract: Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long-horizon tasks. However, the RL-trained agents often struggle in tasks that require active exploration and fail to efficiently adapt from trial-and-error experiences. In this paper, we present LaMer, […]
SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting
arXiv:2512.14718v2 Announce Type: replace-cross Abstract: Effective multivariate time series forecasting often benefits from accurately modeling complex inter-variable dependencies. However, existing attention- or graph-based methods face three key issues: (a) strong temporal self-dependencies are often disrupted by irrelevant variables; (b) softmax normalization ignores and reverses negative correlations; (c) variables struggle to perceive their temporal positions. To […]
Emergent Bias and Fairness in Multi-Agent Decision Systems
arXiv:2512.16433v1 Announce Type: cross Abstract: Multi-agent systems have demonstrated the ability to improve performance on a variety of predictive tasks by leveraging collaborative decision making. However, the lack of effective evaluation methodologies has made it difficult to estimate the risk of bias, making deployment of such systems unsafe in high stakes domains such as consumer […]
Voice-Interactive Surgical Agent for Multimodal Patient Data Control
arXiv:2511.07392v3 Announce Type: replace-cross Abstract: In robotic surgery, surgeons fully engage their hands and visual attention in procedures, making it difficult to access and manipulate multimodal patient data without interrupting the workflow. To overcome this problem, we propose a Voice-Interactive Surgical Agent (VISA) built on a hierarchical multi-agent framework consisting of an orchestration agent and […]
Beyond Rate Coding: Surrogate Gradients Enable Spike Timing Learning in Spiking Neural Networks
arXiv:2507.16043v3 Announce Type: replace-cross Abstract: The surrogate gradient descent algorithm enabled spiking neural networks to be trained to carry out challenging sensory processing tasks, an important step in understanding how spikes contribute to neural computations. However, it is unclear the extent to which these algorithms fully explore the space of possible spiking solutions to problems. […]
Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models
arXiv:2511.07503v3 Announce Type: replace-cross Abstract: The increased availability of genetic data has transformed genomics research, but raised many privacy concerns regarding its handling due to its sensitive nature. This work explores the use of language models (LMs) for the generation of synthetic genetic mutation profiles, leveraging differential privacy (DP) for the protection of sensitive genetic […]
Neural emulation of gravity-driven geohazard runout
arXiv:2512.16221v1 Announce Type: cross Abstract: Predicting geohazard runout is critical for protecting lives, infrastructure and ecosystems. Rapid mass flows, including landslides and avalanches, cause several thousand deaths across a wide range of environments, often travelling many kilometres from their source. The wide range of source conditions and material properties governing these flows makes their runout […]
An Information-Theoretic Framework for Robust Large Language Model Editing
arXiv:2512.16227v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become indispensable tools in science, technology, and society, enabling transformative advances across diverse fields. However, errors or outdated information within these models can undermine their accuracy and restrict their safe deployment. Developing efficient strategies for updating model knowledge without the expense and disruption of full […]