arXiv:2512.11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified…
arXiv:2606.09377v2 Announce Type: replace-cross Abstract: Formal neural network verification — proving that a network satisfies safety properties for *all* inputs…
arXiv:2604.15414v2 Announce Type: replace-cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on emph{single-model…
arXiv:2605.23595v3 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems,…
arXiv:2602.10172v2 Announce Type: replace-cross Abstract: Reconstructing the early universe from the evolved present-day universe is a challenging and computationally demanding…
arXiv:2606.08948v1 Announce Type: cross Abstract: Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training…
arXiv:2606.04409v2 Announce Type: replace-cross Abstract: Modern deep neural networks usually have large parameter scales and nonlinear hierarchical structures, and they…
arXiv:2606.09178v1 Announce Type: cross Abstract: Multilingual safety evaluation of large language models (LLMs) has predominantly relied on direct translation (DT)…
arXiv:2606.07549v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and agent workflows have shown strong promise…
arXiv:2606.07379v2 Announce Type: replace-cross Abstract: A growing failure mode in agent evaluation and training is that models can achieve high…
