An in-depth exploration of world models in AI, covering their definition, implementation approaches (generative vs predictive), and practical applications from autonomous vehicles to interactive environments and agent …
LeWorldModel introduces the first stable end-to-end Joint Embedding Predictive Architecture (JEPA) that learns world models from raw pixels using only two loss terms, achieving 48× faster planning than …
This paper presents Janus-Q, a novel framework that uses hierarchical-gated reward modeling to train large language models for event-driven financial trading, achieving superior performance by directly mapping financial …
This comprehensive guide examines the complete lifecycle of code large language models, from pre-training and supervised fine-tuning to reinforcement learning and deployment as autonomous agents. The paper provides …
LeWorldModel introduces a stable end-to-end method for learning latent world models from raw pixels using only two loss terms, achieving competitive planning performance while being 48× faster than foundation-model-based …
AutoResearch-RL presents a framework where reinforcement learning agents autonomously conduct neural architecture and hyperparameter research without human supervision, using PPO to optimize code modifications based on …
Apple researchers present Ferret-UI Lite, a compact 3B multimodal language model designed for on-device GUI automation across mobile, web, and desktop platforms. The model achieves competitive performance through curated …
GEPA introduces a novel prompt optimization approach that uses natural language reflection and Pareto-based evolutionary search to optimize compound AI systems, achieving superior performance compared to reinforcement …
