arXiv:2511.02042v1 Announce Type: cross Abstract: Rare events such as financial crashes, climate extremes, and biological anomalies are notoriously difficult to model due to their scarcity and heavy-tailed distributions. Classical deep generative models often struggle to capture these rare occurrences, either collapsing low-probability modes or producing poorly calibrated uncertainty estimates. In this work, we propose the […]
Stochastic Models and Estimation of Undetected Infections in the Transmission of Zika Virus
arXiv:2511.01920v1 Announce Type: new Abstract: Zika fever, a mosquito-borne viral disease with potential severe neurological complications and birth defects, remains a significant public health concern. The epidemiological models often oversimplify the dynamics of Zika transmission by assuming immediate detection of all infected cases. This study provides an enhanced SEIR (Susceptible-Exposed-Infectious-Recovered) model to incorporate partial information […]
Geometric Data Valuation via Leverage Scores
arXiv:2511.02100v1 Announce Type: cross Abstract: Shapley data valuation provides a principled, axiomatic framework for assigning importance to individual datapoints, and has gained traction in dataset curation, pruning, and pricing. However, it is a combinatorial measure that requires evaluating marginal utility across all subsets of the data, making it computationally infeasible at scale. We propose a […]
The impact of nonheritable variation in division rates on population growth across environments
arXiv:2511.01905v1 Announce Type: new Abstract: We analyse a series of bacterial growth models with in-built inter-individual variation in rates of cell division. We show that this variation leads to reduced population growth in favorable regimes and reduced population killing in detrimental environments. By treating environmental stress as a model parameter, we then show that the […]
MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation
arXiv:2511.02193v1 Announce Type: cross Abstract: Accurate detection of retinal vessels plays a critical role in reflecting a wide range of health status indicators in the clinical diagnosis of ocular diseases. Recently, advances in deep learning have led to a surge in retinal vessel segmentation methods, which have significantly contributed to the quantitative analysis of vascular […]
Mirror-Neuron Patterns in AI Alignment
arXiv:2511.01885v1 Announce Type: new Abstract: As artificial intelligence (AI) advances toward superhuman capabilities, aligning these systems with human values becomes increasingly critical. Current alignment strategies rely largely on externally specified constraints that may prove insufficient against future super-intelligent AI capable of circumventing top-down controls. This research investigates whether artificial neural networks (ANNs) can develop patterns […]
Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live
arXiv:2511.02230v1 Announce Type: cross Abstract: Agentic LLM applications interleave LLM generation requests with tool calls. These tool calls break the continuity of the workflow by creating pauses between LLM requests, bringing many challenges for the serving system, especially under multi-turn scenarios. Each pause potentially causes KV cache eviction and extra waiting time before entering the […]
HyperHELM: Hyperbolic Hierarchy Encoding for mRNA Language Modeling
arXiv:2509.24655v2 Announce Type: replace-cross Abstract: Language models are increasingly applied to biological sequences like proteins and mRNA, yet their default Euclidean geometry may mismatch the hierarchical structures inherent to biological data. While hyperbolic geometry provides a better alternative for accommodating hierarchical data, it has yet to find a way into language modeling for mRNA sequences. […]
Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning
arXiv:2511.02304v1 Announce Type: cross Abstract: We study the problem of learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution. In this setting, using automata to represent tasks enables the decomposition of complex tasks into simpler sub-tasks that can be assigned to agents. However, existing approaches remain sample-inefficient and are limited to […]
Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing
arXiv:2511.02071v1 Announce Type: new Abstract: Scientific experiment and manufacture rely on complex, multi-step procedures that demand continuous human expertise for precise execution and decision-making. Despite advances in machine learning and automation, conventional models remain confined to virtual domains, while real-world experiment and manufacture still rely on human supervision and expertise. This gap between machine intelligence […]