newsResearchers introduce K²-Agent, a hierarchical AI framework for mobile device control that separates 'know-what' and 'know-how' knowledge to achieve 76.1% success rate on AndroidWorld benchmark. The system uses a high-level reasoner for task planning and low-level executor for skill execution, showing strong generalization across different models and tasks.
Researchers introduce HiMAC, a hierarchical reinforcement learning framework that improves LLM agent performance on long-horizon tasks by separating macro-level planning from micro-level execution. The approach demonstrates state-of-the-art results across multiple environments, showing that structured hierarchy is more effective than simply scaling model size for complex agent tasks.
Researchers propose TARA (Taxonomy-Aware Representation Alignment), a new method to improve Large Multimodal Models' ability to recognize visual categories in hierarchical taxonomies. The approach aligns visual features with biology foundation models to enable better recognition of both known and novel biological categories.