Current world models lack a unified and controlled setting for systematic evaluation, making it difficult to assess whether they truly capture the underlying ru...
Language models have seen enormous progress on advanced benchmarks in recent years, but much of this progress has only been possible by using more costly models...
Deep learning approaches to object detection have achieved reliable detection of specific object classes in images. However, extending a model's detection capab...
Inverse heat problems refer to the estimation of material thermophysical properties given observed or known heat diffusion behaviour. Inverse heat problems have...
This paper studies the role of activation functions in learning modular addition with two-layer neural networks. We first establish a sharp expressivity gap: si...
Offline reinforcement learning (RL) enables agents to learn optimal policies from pre-collected datasets. However, datasets containing suboptimal and fragmented...
Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handlin...
We introduce SuperIntelliAgent, an agentic learning framework that couples a trainable small diffusion model (the learner) with a frozen large language model (t...
Recent advances in generative world models have enabled remarkable progress in creating open-ended game environments, evolving from static scene synthesis towar...
Recent advances in text-to-video (T2V) and image-to-video (I2V) models, have enabled the creation of visually compelling and dynamic videos from simple textual ...
Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising capabilities for au...
Underwater object tracking is challenging due to wavelength dependent attenuation and scattering, which severely distort appearance across depths and water cond...
