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We develop data driven algorithms for maintaining $N$ independent identical machines under a \textit{block replacement policy}, in which each machine is replaced upon failure and all machines are jointly replaced at regular intervals of length $k$. The goal is to learn the cost minimizing interval $k^ $ from operational data when the lifetime distribution is unknown. At each decision epoch, the operator selects $k...
Recent advances in Tool Integrated Large Language Models have made web search a core capability of information seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single and multi agent systems can become trapped in repetitive loops, wasting search budgets and ultimately compromising the quality and...
Motor imagery based brain–computer interfaces (BCIs) have attracted significant attention due to their potential applications in assistive technologies, neurorehabilitation, and wearable human–machine interaction systems. However, practical implementation of EEG based BCIs remains challenging because high density EEG recordings increase hardware complexity, prolong setup time, and introduce substantial spatial red...
Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the dynamics are unknown and only a subset of the state variables are measured. We propose a hybrid neural physics framework in which the known components of the ODE are kept explicit and the missing components are represented by a neural ne...
Direct hand driven teleoperation maps an operator's hand motion to robot end effector commands at every frame, enabling precise control, but it requires constant monitoring and correction during approach, grasp, and placement, which can be slow and fatiguing. For repetitive pick and place tasks, supervisory (goal based) teleoperation simplifies this process: the operator specifies goals/waypoints, and the robot ex...
On policy distillation is an alternative post training method in reinforcement learning that alleviates the constraints imposed by reward models by providing token level supervision from a teacher model. Although on policy distillation has been studied and applied across various settings, its fundamental design remains underexplored. In this paper, we introduce a new distillation reward, termed the delta signal, i...
In this paper, we propose a software platform that enables respondents to complete different selective attention test variants, locally or remotely, while at the same time collecting their electroencephalogram measurements. The main goal is to provide an applied software solution for a platform in the domain of telepsychology to go beyond the current use of telecommunication tools in this field. The platform recon...
Motor imagery electroencephalography (MI EEG) decoding remains challenging due to low signal to noise ratio and complex temporal spectral characteristics. This study aims to develop a robust deep learning framework for effective EEG representation learning. We propose EEG DBNet, a dual branch neural network that jointly models temporal dynamics and spectral representations of EEG signals. The model integrates loca...
We present NeuronSoup, a neural computation architecture that replaces synchronous layer by layer processing with asynchronous, delay mediated signal propagation through a pool of shared neurons. Each path in the network routes a continuous valued signal from one input neuron to one output neuron through a variable number of intermediate hidden neurons. Hidden neurons are physically shared across paths: when two p...
Recent robot foundation models operate with single step or short history visuomotor context. We introduce Test Time Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state of the art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one shot in context imitation fr...
The growing use of Bitcoin as a decentralized digital asset and investment tool has sparked strong interest in understanding its market behavior. This study presents a new approach to analyze Bitcoin market sentiment by combining on chain and financial data with social media posts. Unlike models that aim to predict prices, this work focuses on explaining market sentiment using blockchain transactions, historical p...
World action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction. This coupling is often viewed as a source of robustness, interpretability, and safety, as a robot's action can in principle be checked against its imagined future. In this paper, we show that this assumpt...
Evidence synthesis is crucial for turning primary research into reliable knowledge for science, medicine, education, and policy. Yet, quantitative evidence synthesis remains largely manual and difficult to scale. Here, we introduce AutoSynthesis, an end to end multi agent system for automated meta analysis. Given a research question in natural language, AutoSynthesis formulates a search strategy, retrieves scienti...
Near infrared spectroscopy (NIR and Vis–NIR) is widely used for rapid, non destructive analysis in food, agriculture, pharmaceutical, process analytical technology (PAT), and bioprocess monitoring applications. Yet, deep learning studies in NIR chemometrics often report conflicting conclusions about convolutional neural network (CNN) design: small versus large kernels, shallow versus deep architectures, raw spectr...
Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart centric and provide limited evidence of understanding of scientific visualization (SciVis). We benchmark six MLLMs on the scientific visualization literacy assessment test, a standardized SciVis literacy assessment comprising 49 items based on 18 scientific visualizations and ill...
A common bottleneck in two stage recommendation is embedding staleness: when a user rates a new item, their embedding remains fixed until the next retrain cycle. We propose mutable sketches, which store each user's preferences in a KP tree (a sparse segment tree with sum aggregation), fit a low rank projection once, and recompute embeddings on the fly as ratings arrive. We prove that each new observation monotonic...
Frailty assessments are crucial to evaluate the risk of adverse events and the health and social care needs of older adults, yet their administration remains resource intensive and typically relies on coarse clinical outcomes, such as task completion times, which may overlook biomechanical indicators of functional decline. To address this, we present a robotic framework that guides older adults through standardise...
OBJECTIVE: The vision based grasping brain network integrates visual perception with cognitive and motor processes for visuomotor tasks. While invasive recordings have successfully decoded localized neural activity related to grasp type planning and execution, macroscopic neural activation patterns captured by noninvasive electroencephalography (EEG) remain far less understood. METHODS: We introduce a vision based...
Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate. Prior work on poisoning pretraining data has largely exploited established data sources such as Wikipedia, which do not represent the large scale and heterogeneity typical of pretraining corpora, and has ignored the interaction between poisoned data and data curation pipelines. We demonstrate that poisoning...
AI benchmarks increasingly leverage item level statistical models, particularly item response theory (IRT), to estimate model capabilities, rank systems, select informative examples, and diagnose benchmark quality. However, AI benchmark data often departs from the data regime of human testing, for which standard IRT estimation tools were originally developed: benchmarks typically involve fewer evaluated models, fa...
