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arXiv:2604.01694v1 Announce Type: new Abstract: Minor Component Adaptation (MiCA) is a novel parameter-efficient fine-tuning method for large language models that focuses on adapting underutilized subspaces …
arXiv:2604.01683v1 Announce Type: new Abstract: Standard scaled dot-product attention computes scores from static, independent projections of the input. We show that evolving queries and keys …
arXiv:2604.01653v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics. However, modeling how these states evolve in …
arXiv:2604.01651v1 Announce Type: new Abstract: An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, …
arXiv:2604.01634v1 Announce Type: new Abstract: Real-world reasoning often requires combining information across modalities, connecting textual context with visual cues in a multi-hop process. Yet, most …
arXiv:2604.01622v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel, non-autoregressive text generation, yet existing DLM mixture-of-experts (MoE) models inherit token-choice (TC) routing from …
arXiv:2604.01613v1 Announce Type: new Abstract: In reinforcement learning (RL), temporal difference (TD) errors are widely adopted for optimizing value and policy functions. However, since the …
arXiv:2604.01601v1 Announce Type: new Abstract: We investigate training strategies that co-develop in-context learning (ICL) and in-weights learning (IWL), and the ability to switch between them …
arXiv:2604.01597v1 Announce Type: new Abstract: Traditional RL algorithms like Proximal Policy Optimization (PPO) typically train on the entire rollout buffer, operating under the assumption that …
arXiv:2604.01595v1 Announce Type: new Abstract: Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, …
arXiv:2604.01587v1 Announce Type: new Abstract: Uncertainty propagation in high-dimensional nonlinear dynamic structural systems is pivotal in state-of-the-art performance-based design and risk assessment, where uncertainties from …
arXiv:2604.01577v1 Announce Type: new Abstract: We extend the recent latent recurrent modeling to sequential input streams. By interleaving fast, recurrent latent updates with self-organizational ability …
