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arXiv:2603.00010v1 Announce Type: new Abstract: Transit Network Design is a well-studied problem in the field of transportation, typically addressed by solving optimization models under fixed …
arXiv:2603.00037v1 Announce Type: new Abstract: Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce …
arXiv:2603.00039v1 Announce Type: new Abstract: LLM-as-a-judge ensembles are the standard paradigm for scalable evaluation, but their aggregation mechanisms suffer from a fundamental flaw: they implicitly …
arXiv:2603.00040v1 Announce Type: new Abstract: Achieving reliable 4-bit attention is a prerequisite for end-to-end FP4 computation on emerging FP4-capable GPUs, yet attention remains the main …
arXiv:2603.00041v1 Announce Type: new Abstract: Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional …
arXiv:2603.00042v1 Announce Type: new Abstract: We identify the Spectral Energy Gain in extreme model compression, where low-rank binary approximations outperform tiny-rank floating-point baselines for heavy-tailed …
arXiv:2603.00043v1 Announce Type: new Abstract: This paper presents a novel approach to reinforcement learning (RL) for control systems that provides probabilistic stability guarantees using finite …
arXiv:2603.00044v1 Announce Type: new Abstract: Advancing trustworthy AI requires principled software engineering approaches to model evaluation. Graph Neural Networks (GNNs) have achieved remarkable success in …
arXiv:2603.00045v1 Announce Type: new Abstract: Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the "factorization barrier": the assumption that …
arXiv:2603.00046v1 Announce Type: new Abstract: Medical multi-modal learning is critical for integrating information from a large set of diverse modalities. However, when leveraging a high …
arXiv:2603.00049v1 Announce Type: new Abstract: Self-Supervised Learning (SSL) has shifted from pixel-level reconstruction to latent space prediction, spearheaded by the Joint Embedding Predictive Architecture (JEPA). …
arXiv:2603.00052v1 Announce Type: new Abstract: Surrogate models are widely used in mechanical design and manufacturing process optimization, where high-fidelity computational models may be unavailable or …