arXiv:2603.25855v1 Announce Type: new Abstract: Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mechanisms is critical for therapeutic target prioritization. The recently proposed Knowledge Graph GWAS (KGWAS) framework addresses this challenge…
arXiv:2603.25839v1 Announce Type: new Abstract: Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through the lens of the Minimum Description Length…
arXiv:2603.25813v1 Announce Type: new Abstract: We present MAGNET (Model Autonomously Growing Network), a decentralized system for autonomous generation, training, and serving of domain-expert language models across commodity hardware. MAGNET integrates four components: (1) autoresearch, an autonomous ML research pipeline that…
arXiv:2503.22886v2 Announce Type: replace Abstract: Recent advancements in imitation learning have led to transformer-based behavior foundation models (BFMs) that enable multi-modal, human-like control for humanoid agents. While excelling at zero-shot generation of robust behaviors, BFMs often require meticulous prompt engineering…
arXiv:2603.25901v1 Announce Type: new Abstract: Defensive coverage schemes in the National Football League (NFL) represent complex tactical patterns requiring coordinated assignments among defenders who must react dynamically to the offense’s passing concept. This paper presents a factorized attention-based transformer model…
arXiv:2510.05516v2 Announce Type: replace Abstract: Bayesian optimization (BO) is effective for expensive black-box problems but remains challenging in high dimensions. We propose NeST-BO, a curvature-aware local BO method that targets a (modified) Newton step by jointly learning gradient and Hessian…
arXiv:2603.25916v1 Announce Type: new Abstract: We study dynamic regret minimization in unconstrained adversarial linear bandit problems. In this setting, a learner must minimize the cumulative loss relative to an arbitrary sequence of comparators $boldsymbol{u}_1,ldots,boldsymbol{u}_T$ in $mathbb{R}^d$, but receives only point-evaluation…
arXiv:2601.21419v2 Announce Type: replace Abstract: Recent advances in diffusion and flow matching models have highlighted a shift in the preferred prediction target — moving from noise ($varepsilon$) and velocity (v) to direct data (x) prediction — particularly in high-dimensional settings.…
arXiv:2603.25923v1 Announce Type: new Abstract: Deep learning models have shown promise in EEG-based outcome prediction for comatose patients after cardiac arrest, but their reliability is often compromised by subtle forms of data leakage. In particular, when long EEG recordings are…
arXiv:2603.22885v3 Announce Type: replace Abstract: BACKGROUND: General aviation fleet expansion demands intelligent health monitoring under computational constraints. Real-world aircraft health diagnosis requires balancing accuracy with computational constraints under extreme class imbalance and environmental uncertainty. Existing end-to-end approaches suffer from the…
