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arXiv:2603.10279v1 Announce Type: new Abstract: Aligning generative recommender systems to user preferences via post-training is critical for closing the gap between next-item prediction and actual …
arXiv:2603.10281v1 Announce Type: new Abstract: While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization algorithms such …
arXiv:2603.10283v1 Announce Type: new Abstract: Geometry-grounded learning asks models to respect structure in the problem domain rather than treating observations as arbitrary vectors. Motivated by …
arXiv:2603.10284v1 Announce Type: new Abstract: A key challenge in travel demand analysis is the presence of unobserved factors that may generate non-causal dependencies, obscuring the …
arXiv:2603.10298v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) and their ability to capture semantic relationships has led to their adoption …
arXiv:2603.10299v1 Announce Type: new Abstract: This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary …
arXiv:2603.10301v1 Announce Type: new Abstract: A basic unanswered question in neural network training is: what is the best learning rate schedule shape for a given …
arXiv:2603.10302v1 Announce Type: new Abstract: A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to …
arXiv:2603.10305v1 Announce Type: new Abstract: Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information …
arXiv:2603.10341v1 Announce Type: new Abstract: Federated active learning (FAL) seeks to reduce annotation cost under privacy constraints, yet its effectiveness degrades in realistic settings with …
arXiv:2603.10377v1 Announce Type: new Abstract: Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose …
arXiv:2603.10379v1 Announce Type: new Abstract: This paper presents a novel extension of neural scaling laws to Mixture-of-Experts (MoE) models, focusing on the optimal allocation of …