arXiv:2608.20440v1 Announce Type: new Abstract: Authentication of edible oils in processed foods is important for food quality, fraud prevention, and regulatory compliance. This study establishes an integrated Raman spectroscopy and machine-learning framework that links intrinsic spectral organization, interpretable classification, and…
arXiv:2608.20428v1 Announce Type: new Abstract: We study the stability of minimal representations of controlled stochastic processes (in particular, transducers) under perturbations. This question is motivated by recent experiments finding predictive-state structure in the latent representations of neural networks. We consider…
arXiv:2608.20447v1 Announce Type: new Abstract: Feature selection is a highly relevant task in a data-driven knowledge discovery project. Several techniques have been developed aiming at finding the features that influence most an outcome to predict, including mutual information and, in…
arXiv:2505.11924v4 Announce Type: replace-cross Abstract: Intrinsic moral self-correction refers to the phenomenon where a language model refines its ethical judgments or aligns its outputs purely through prompting. While effective across diverse tasks, its mechanism remains unclear. We hypothesize intrinsic moral…
arXiv:2608.20480v1 Announce Type: new Abstract: Optimal learners are tailored to exploit the i.i.d. data assumption underlying the classic PAC model. What if an i.i.d. training sample were corrupted with correctly labeled examples drawn from an otherwise unrelated, even adversarial source?…
arXiv:2606.02507v2 Announce Type: replace-cross Abstract: Inverse materials design is shifting materials discovery from forward prediction toward targeted proposal of candidates that satisfy objectives under physical constraints. Here, we review advances in generative crystal structure modeling, multimodal learning, and closed-loop design…
arXiv:2608.20488v1 Announce Type: new Abstract: AI tools increasingly support tasks across the scientific research cycle, from experiment design and manuscript preparation to peer review. At the same time, the continuing growth in conference submissions has increased the burden on meta-reviewers,…
arXiv:2608.21134v1 Announce Type: cross Abstract: Deploying vision-language models (VLMs) on mobile devices is challenging due to their significant memory and compute requirements. We present a framework for quantizing VLMs for efficient inference on resource-constrained hardware. Our approach combines a quantization…
arXiv:2608.20497v1 Announce Type: new Abstract: Deploying high-accuracy neural networks on resource-constrained edge devices remains challenging, as existing approaches treat training, compression, and hardware synthesis as separate stages, leaving a gap between software-trained models and efficient end-to-end deployment with limited support…
arXiv:2608.21262v1 Announce Type: cross Abstract: Many machine-learning systems set a threshold at a quantile of a calibration set: conformal predictors that promise 90% coverage by drawing their cutoff at the calibration set’s 90th percentile, abstention gates that decline to answer…
