arXiv:2602.17689v1 Announce Type: new Abstract: Medical vision-language models show strong potential for joint reasoning over medical images and clinical text, but their performance often degrades under domain shift caused by variations in imaging devices, acquisition protocols, and reporting styles. Existing…
arXiv:2503.19605v4 Announce Type: replace Abstract: Understanding and certifying the generalization performance of machine learning algorithms — i.e. obtaining theoretical estimates of the test error from a finite training sample — is a central theme of statistical learning theory. Among the…
arXiv:2602.17691v1 Announce Type: new Abstract: Quantized language models face a fundamental dilemma: low sampling temperatures yield repetitive, mode-collapsed outputs, while high temperatures (T > 2.0) cause trajectory divergence and semantic incoherence. We present HELIX, a geometric framework that decouples output…
arXiv:2509.06484v2 Announce Type: replace Abstract: The excess Gibbs energy plays a central role in chemical engineering and chemistry, providing a basis for modeling thermodynamic properties of liquid mixtures. Predicting the excess Gibbs energy of multi-component mixtures solely from molecular structures…
arXiv:2602.17692v1 Announce Type: new Abstract: In this paper, we introduce textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents with closed-loop interaction. Existing unlearning methods target parameters alone, leaving two critical gaps: (i) parameter-memory…
arXiv:2511.19269v2 Announce Type: replace Abstract: Diffusion Language Models (DLMs) offer a promising parallel generation paradigm but suffer from slow inference due to numerous refinement steps and the inability to use standard KV caching. We introduce CDLM (Consistency Diffusion Language Models),…
arXiv:2508.04581v2 Announce Type: replace-cross Abstract: Large language models have revolutionized AI applications, yet their high computational and memory demands hinder their widespread deployment. Existing compression techniques focus on intra-block optimizations (e.g., low-rank approximation or attention pruning), while the repetitive layered…
arXiv:2510.08570v2 Announce Type: replace Abstract: Neural networks are famously nonlinear. However, linearity is defined relative to a pair of vector spaces, $f:X to Y$. Leveraging the algebraic concept of transport of structure, we propose a method to explicitly identify non-standard…
arXiv:2601.11924v2 Announce Type: replace Abstract: We study cooperative stochastic multi-armed bandits with vector-valued rewards under adversarial corruption and limited verification. In each of $T$ rounds, each of $N$ agents selects an arm, the environment generates a clean reward vector, and…
arXiv:1803.09319v2 Announce Type: replace Abstract: Deep neural networks are often used to implement powerful generative models for real-world data. Notable applications include image denoising, as well as other classical inverse problems like compressed sensing and super-resolution. To provide a rigorous…
