artificial intelligence

SprintX: mitigating data scarcity in antibody design via synthetic structural prior knowledge dataset.

Across downstream tasks including antibody generation, computational binding-affinity prediction, and binding-interface prediction, models pretrained with SprintX showed meaningful improvements in structural plausibility and competitive performance on computational binding-affinity and binding-interface prediction benc...

TOURISM 5.0 IN PRACTICE: NAVIGATING THE TENSIONS BETWEEN DIGITAL EFFICIENCY AND LIVED AUTHENTICITY IN THE SAUDI CONTEXT

The Tourism 5.0 paradigm is emerging as a pivotal response to the limits of a purely efficiency-oriented digitalisation, by placing people and sustainability back at the centre of innovation to create a holistic and responsible tourism ecosystem. Within the framework of Vision 2030, Saudi Arabia has adopted this orient...

Artificial intelligence and machine learning models for electrolyzers and fuel cells optimization: A review on empowering sustainable hydrogen economy

Never-Skilling in Dentistry: When Artificial Intelligence Performs the Thinking Before Learners Learn to Think

Joint Latent Reasoning and Observation for Agentic Video Understanding

Effective video agents must learn not only how to answer, but also where to look next. Transferring this capability to smaller models is challenging because each observation serves two purposes: supporting an answer and guiding the search for further evidence. Textual descriptions may capture what is currently visible...

A Sign Law for Entropy Drift under Sampler Mismatch in RLVR

In reinforcement learning with verifiable rewards (RLVR), whether entropy rises or falls can depend on choices that matter little under exact sampling, such as whether over-length responses are masked or counted as failures. We trace this to sampler mismatch: rollouts are drawn with top-k/top-p truncation or at a tempe...

POD-RoPE: Rotary Position Encoding over Physical Observation Distributions for Embodied AI

Embodied perception requires positional representations that capture both physical geometry and the spatial and temporal extent of observations. We introduce POD-RoPE (Physical Observation Distribution RoPE), which represents token observation footprints as distributions over camera centers, viewing directions, and sam...

MarineLife-16K: An Attribute-Rich Video-Text Dataset for Motion-Aware Marine Video Understanding

Marine video understanding is crucial for a wide range of real-world applications. However, existing marine video datasets suffer from limitations, including the limited number of data samples, fine-grained text descriptions, and detailed visual attributes. To address these limitations, we introduce a large-scale, vide...

Value-Rectified Distillation for Flow-based Offline Reinforcement Learning

Value-Rectified Distillation is proposed, a novel framework that reformulates offline policy distillation as a value-guided generative trajectory alignment problem and improves existing generative baselines across diverse continuous control and planning tasks on the D4RL, OGBench, and Minari benchmarks, especially over...

Rethinking Time Series Tokenization from a Frequency Perspective

TimeFT is proposed, a nearly distortion-free frequency-based tokenizer for time series data that is parameter-free, incurs negligible computational complexity, and can serve as a drop-in replacement for existing tokenizers.

A General Topological Symmetry Index for Critical Orbits In Neural Network Loss Landscapes

A systematic understanding of the global structure of neural-network loss landscapes remains challenging due to their high dimensionality. Symmetries arising from network architectures and learning objectives provide a way to investigate aspects of this structure. Although a growing number of studies have drawn attenti...

PACE: Pareto-Adaptive Compression for Efficient Native MLLMs

Native multimodal large language models (native MLLMs) jointly encode visual and linguistic sequences within a unified autoregressive Transformer, emerging as a promising paradigm for multimodal understanding. Unlike conventional ViT-based MLLMs relying on external vision encoders, ViT-free native MLLMs tokenize pixel...