Latent reasoning represents a new development in Transformer language models that has shown potential in compressing reasoning lengths compared to chain-of-thou...
Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applicatio...
If a language model cannot reliably disclose its AI identity in expert contexts, users cannot trust its competence boundaries. This study examines self-transpar...
Cyclic peptides are promising modalities for targeting intracellular sites; however, cell-membrane permeability remains a key bottleneck, exacerbated by limited...
This study proposes a risk prediction method based on a Multi-Scale Temporal Alignment Network (MSTAN) to address the challenges of temporal irregularity, sampl...
Vision Language Action models have significantly advanced general purpose robotic manipulation by harnessing large scale pretrained vision and language represen...
Human activity recognition (HAR) from inertial sensors is essential for ubiquitous computing, mobile health, and ambient intelligence. Conventional deep models ...
Real-world data, for example in climate applications, often consists of spatially gridded time series data or data with comparable structure. While the underlyi...
Obtaining safety guarantees for reinforcement learning is a major challenge to achieve applicability for real-world tasks. Safety shields extend standard reinfo...
A fundamental theoretical question in network analysis is to determine under which conditions community recovery is possible in polynomial time in the Stochasti...
The key limitation of the verification performance lies in the ability of error detection. With this intuition we designed several variants of pessimistic verif...
Unlike text, speech conveys information about the speaker, such as gender, through acoustic cues like pitch. This gives rise to modality-specific bias concerns....
