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arXiv:2601.22301v1 Announce Type: new Abstract: Traditional rendering pipelines rely on complex assets, accurate materials and lighting, and substantial computational resources to produce realistic imagery, yet...
arXiv:2601.22302v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in...
arXiv:2601.22305v1 Announce Type: new Abstract: Automatic workflow generation is the process of automatically synthesizing sequences of LLM calls, tool invocations, and post-processing steps for complex...
arXiv:2601.22307v1 Announce Type: new Abstract: We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual)...
arXiv:2601.22308v1 Announce Type: new Abstract: Regression models are widely used in industrial processes, engineering and in natural and physical sciences, yet their robustness to poisoning...
arXiv:2601.22311v1 Announce Type: new Abstract: Large language model (LLM)-based agents exhibit strong step-by-step reasoning capabilities over short horizons, yet often fail to sustain coherent behavior...
arXiv:2601.22312v1 Announce Type: new Abstract: Large language models are increasingly applied to materials science reasoning, yet their behavior under physically structured distribution shifts remains poorly...
arXiv:2601.22313v1 Announce Type: new Abstract: Large Language Models (LLMs) are rarely static and are frequently updated in practice. A growing body of alignment research has...
arXiv:2601.22315v1 Announce Type: new Abstract: Many real-world optimization problems involve an expensive ground-truth oracle (e.g., human evaluation, physical experiments) and a cheap, low-fidelity prediction oracle...
arXiv:2601.22317v1 Announce Type: new Abstract: Recovering missing flows on the edges of a network, while exactly respecting local conservation laws, is a fundamental inverse problem...
arXiv:2601.22318v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly accessed as remotely hosted services by edge and enterprise clients that cannot run frontier...
arXiv:2601.22320v1 Announce Type: new Abstract: We study continual mean estimation, where data vectors arrive sequentially and the goal is to maintain accurate estimates of the...
arXiv:2601.22322v1 Announce Type: new Abstract: Indoor localization is a critical enabler for a wide range of location-based services in smart environments, including navigation, asset tracking,...
arXiv:2601.22323v1 Announce Type: new Abstract: Model routing chooses which language model to use for each query. By sending easy queries to cheaper models and hard...
arXiv:2601.22324v1 Announce Type: new Abstract: Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision...
arXiv:2601.22326v1 Announce Type: new Abstract: Monitoring the performance of classification models in production is critical yet challenging due to strict labeling budgets, one-shot batch acquisition...
arXiv:2601.22327v1 Announce Type: new Abstract: Molecular representations fundamentally shape how machine learning systems reason about molecular structure and physical properties. Most existing approaches adopt a...
arXiv:2601.22328v1 Announce Type: new Abstract: Recovering governing equations from data is central to scientific discovery, yet existing methods often break down under noisy, partial observations,...
arXiv:2601.22329v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly positioned as decision engines for hiring, healthcare, and economic judgment, yet real-world human judgment...
arXiv:2601.22331v1 Announce Type: new Abstract: Cell Painting is a microscopy-based, high-content imaging assay that produces rich morphological profiles of cells and can support drug discovery...
arXiv:2601.22334v1 Announce Type: new Abstract: Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential privacy guarantees....
arXiv:2601.22335v1 Announce Type: new Abstract: The knowledge gradient is a popular acquisition function in Bayesian optimization (BO) for optimizing black-box objectives with noisy function evaluations....
arXiv:2601.22338v1 Announce Type: new Abstract: LLMs are popular among clinicians for decision-support because of simple text-based interaction. However, their impact on clinicians' performance is ambiguous....
arXiv:2601.22339v1 Announce Type: new Abstract: Modern supply chains must balance high-speed logistics with environmental impact and security constraints, prompting a surge of interest in AI-enabled...