arXiv:2601.10763v2 Announce Type: replace-cross Abstract: A probabilistic framework for irreversible kinetics is proposed in which a constrained path functional $mathcal J$ encodes constitutive physics and observations…
arXiv:2410.01669v3 Announce Type: replace-cross Abstract: Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix of input data to leverage correlation information as pairwise connections.…
arXiv:2608.00007v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation. Traditional prompt-based…
arXiv:2608.01691v1 Announce Type: cross Abstract: Detecting a change in a multivariate series answers only the first of two questions; the operational question is which coordinates changed.…
arXiv:2608.00078v1 Announce Type: new Abstract: Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: INT8…
arXiv:2608.00107v1 Announce Type: new Abstract: Agentic systems must repeatedly decide whether to answer directly, decompose a task, invoke a tool, execute code, delegate to a specialist,…
arXiv:2506.07121v2 Announce Type: replace-cross Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible…
arXiv:2604.10727v2 Announce Type: replace Abstract: Classical information-theoretic learning bounds typically rely on KL mutual information and moment-generating-function (MGF) arguments, which are well matched to bounded or…
arXiv:2606.27564v1 Announce Type: cross Abstract: Most approaches for accelerating Markov chain mixing either rely on incorporating expensive geometric information in the proposals, or reduce the per-step…
arXiv:2606.24758v2 Announce Type: replace Abstract: Handling repeated characters in text can be tricky, since they can represent either the correct spelling of a word or informal…
arXiv:2601.01095v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in…
arXiv:2603.25129v2 Announce Type: replace Abstract: While 3D Vision Foundation Models (3DVFMs) have demonstrated remarkable zero-shot capabilities in visual geometry estimation, their direct application to generalizable novel…
arXiv:2603.07475v4 Announce Type: replace-cross Abstract: Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence denoising. Although…
arXiv:2605.30359v2 Announce Type: replace Abstract: Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization. While large language models (LLMs)…
arXiv:2608.01587v1 Announce Type: new Abstract: Machine-learning benchmarks often pair a label that aggregates a long temporal horizon with input observed through one or a few short…
arXiv:2608.00247v1 Announce Type: cross Abstract: Variable importance may describe either intrinsic predictive information in a population or extrinsic importance for a fitted prediction rule. Quantifying the…
arXiv:2608.00030v1 Announce Type: new Abstract: Specialised retrieval agents typically surface higher quality results than general-purpose search, but selecting the optimal agent for a given query remains…
arXiv:2608.00074v1 Announce Type: new Abstract: This paper presents a multimodal machine-learning framework for calibration monitoring, quality assessment, and adaptive acquisition support in archaeological digitisation workflows. The…
arXiv:2608.00079v1 Announce Type: new Abstract: Long-form and real-time talking-head generation remains challenging due to a latency-quality trade-off: inefficient multi-step diffusion prohibits streaming generation, whereas real-time autoregressive…
arXiv:2608.00198v1 Announce Type: new Abstract: Environmental time-series causal discovery requires expert decisions about method choice, conditional-independence tests, lag horizons, sample-size adequacy, multiple-testing control, and evidence interpretation.…
arXiv:2606.17091v2 Announce Type: replace Abstract: Open-ended evolution (OEE) in artificial life is typically driven by uninterpretable, black-box neural-network complexity metrics, leaving life-like systems disconnected from physical…
arXiv:2511.12840v2 Announce Type: replace Abstract: Overparameterized models often generalize well even when they interpolate noisy training data. This is known as benign overfitting. For linear classification,…
arXiv:2605.25344v2 Announce Type: replace Abstract: Dense linear maps carry much of the parameter and computational burden of modern neural networks, yet their dense form leaves the…
arXiv:2510.27313v3 Announce Type: replace-cross Abstract: Generation novelty is a key indicator of an LLM's ability to generalize, yet measuring it against full pretraining corpora is computationally…