arXiv:2605.02937v1 Announce Type: new
Abstract: Deep learning in \emph{de novo} protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synthesize molecular geometries without explicit…
arXiv:2502.04419v3 Announce Type: replace
Abstract: Generating synthetic datasets via large language models (LLMs) has emerged as a promising approach to improve LLM performance. However, LLMs inherently reflect biases in their training data, leading …
arXiv:2604.09111v4 Announce Type: replace-cross
Abstract: Recently, artificial intelligence-based dubbing technology has advanced, enabling automated dubbing (AD) to convert the source speech of a video into target speech in different languages. Howev…
arXiv:2605.02914v1 Announce Type: new
Abstract: A guard model fine-tuned on entirely benign data can lose all safety alignment — not through adversarial manipulation, but through standard domain specialization. We demonstrate this failure across thre…
arXiv:2605.02916v1 Announce Type: new
Abstract: The development of AI-assisted Early Intensive Behavioral Intervention (EIBI) for Autism Spectrum Disorder (ASD) is severely constrained by data scarcity. Furthermore, while Applied Behavior Analysis (AB…
arXiv:2605.02913v1 Announce Type: new
Abstract: Reinforcement learning (RL) has become a central post-training tool for improving the reasoning abilities of large language models (LLMs). In these systems, the rollout, the trajectory sampled from a pro…
arXiv:2605.02906v1 Announce Type: new
Abstract: In the field of software operations, Large Language Models (LLMs) have attracted increasing attention. However, existing research has not yet achieved efficient and effective end-to-end intelligent opera…
arXiv:2605.02917v1 Announce Type: new
Abstract: Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes of physiologically info…
arXiv:2508.14994v3 Announce Type: replace-cross
Abstract: In hazardous and remote environments, robotic systems perform critical tasks demanding improved safety and efficiency. Among these, quadruped robots with manipulator arms offer mobility and ver…
arXiv:2605.02918v1 Announce Type: new
Abstract: Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are often chosen to minimize t…