Academic
arXiv:2602.18591v1 Announce Type: new Abstract: A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL …
arXiv:2602.18600v1 Announce Type: new Abstract: Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing benchmarks remain …
arXiv:2602.18613v1 Announce Type: new Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with …
arXiv:2602.18628v1 Announce Type: new Abstract: Large language models store all learned knowledge in a single, fixed weight vector. Teaching a model new capabilities requires modifying …
arXiv:2602.18637v1 Announce Type: new Abstract: $\textit{Objective.}$ Accurate neural decoding of locomotion holds promise for advancing rehabilitation, prosthetic control, and understanding neural correlates of action. Recent …
arXiv:2602.18639v1 Announce Type: new Abstract: World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-level …
arXiv:2602.18645v1 Announce Type: new Abstract: Time series reasoning tasks often start with a natural language question and require targeted analysis of a time series. Evidence …
arXiv:2602.18647v1 Announce Type: new Abstract: Training diffusion models typically relies on manually tuned noise schedules, which can waste computation on weakly informative noise regions and …
arXiv:2602.18649v1 Announce Type: new Abstract: Grokking -- the abrupt transition from memorization to generalization after extended training -- has been linked to the emergence of …
arXiv:2602.18658v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods, such as LoRA, offer a practical way to adapt large vision and language models to client tasks. …
arXiv:2602.18662v1 Announce Type: new Abstract: Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted …
arXiv:2602.18674v1 Announce Type: new Abstract: We present a theoretical study of the robustness of parameterized networks to random input perturbations. Specifically, we analyze local robustness …
