arXiv:2604.07753v2 Announce Type: replace Abstract: Empowering Large Multimodal Models (LMMs) with image generation often leads to catastrophic forgetting in understanding tasks due to severe gradient conflicts.…
arXiv:2606.28395v1 Announce Type: new Abstract: Recent studies have shown that spatial properties of tumors are critical for understanding disease biology and predicting patient outcomes. These spatial…
arXiv:2606.28459v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) clustering is essential for identifying cell types, but high dimensionality, sparsity, dropout, and technical noise hinder robust…
arXiv:2606.28373v1 Announce Type: new Abstract: Model merging integrates the capabilities of multiple expert models to create strong models for multiple tasks without additional training, thereby reducing…
arXiv:2606.29953v1 Announce Type: new Abstract: LLM-assisted evolutionary search (LES) has emerged as a promising paradigm for automated algorithm design. However, existing methods usually suffer from two…
arXiv:2606.28871v1 Announce Type: new Abstract: Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and…
arXiv:2606.29844v1 Announce Type: cross Abstract: The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language…
arXiv:2606.28719v1 Announce Type: new Abstract: Test-time adaptation (TTA) of vision-language models (VLMs) is essential for their robust deployment in dynamic, real-world environments. However, existing TTA methods…
arXiv:2606.28538v1 Announce Type: new Abstract: We investigate domain adaptation of modern BERT models in the legal domain. We further pre-train ModernBERT on all US court opinions…
arXiv:2606.00616v3 Announce Type: replace Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos. We introduce pause-and-think-T, a reasoning-centric…
arXiv:2403.07711v5 Announce Type: replace Abstract: Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models…
arXiv:2606.28460v1 Announce Type: new Abstract: Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the…
arXiv:2606.15830v2 Announce Type: replace Abstract: CMA-ES is, per run, a local optimizer; multimodal search relies on restart strategies such as IPOP and BIPOP, which draw every…
arXiv:2602.10233v2 Announce Type: replace Abstract: LLM-guided evolutionary computation, most notably AlphaEvolve, has been remarkably successful in discovering novel mathematical constructions by solving challenging optimization problems. The…
arXiv:2606.28854v1 Announce Type: new Abstract: The common factor analytic model is related to Helmholtz and Boltzmann machines, can be conceived as a linear autoencoder, or can…
arXiv:2606.30117v1 Announce Type: cross Abstract: We investigate the reconstruction of holographic duals for strongly coupled quantum field theories in regimes characterized by large hierarchies and the…
arXiv:2606.29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS)…
arXiv:2606.28526v1 Announce Type: new Abstract: The clinical and communication skills of medical students are commonly assessed through Objective Structured Clinical Examinations (OSCEs), which consist of brief…
arXiv:2512.10342v3 Announce Type: replace Abstract: Vision Language Models (VLMs) have shown promising planning capabilities, yet their success remains confined to the text domain, leaving visual decision-making…
arXiv:2606.28396v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar perception is limited by data scarcity: models trained on existing radar datasets fail to generalize to new objects,…
arXiv:2606.28444v1 Announce Type: new Abstract: Classical universal approximation theorems establish the expressive power of sigmoidal multilayer perceptrons, but they do not prescribe how initial weights should…
arXiv:2606.30619v1 Announce Type: cross Abstract: We show that the effective dynamics of the elitist $(1+M)$ genetic algorithm is, in the limit of small mutations, clipped gradient…
arXiv:2606.30450v1 Announce Type: new Abstract: This paper presents the Metabolic Multi-Agent Optimizer (MMAO) as an adaptive metaheuristic built around endogenous resource circulation. The central premise is…
arXiv:2606.28808v1 Announce Type: new Abstract: We study the leading-order fluctuation of stochastic gradient Euler-Maruyama estimators for generalized non-reversible Langevin dynamics. Under structural assumptions tailored to the…