Open-world reinforcement learning challenges agents to develop intelligent behavior in vast exploration spaces. Recent approaches like LS-Imagine have advanced the field by extending imagination horizons through jumpy state transitions, yet remain limited by fixed exploration mechanisms and static jump thresholds that cannot adapt across changing task phases, resulting in inefficient exploration and lower completion rates. Humans demonstrate remarkable capabilities in open-world decision-making through a chain-like process of task decomposition, selective memory utilization, and adaptive uncertainty regulation. Inspired by human decision-making processes, we present Cognitive Predictive Processing (CPP), a novel framework that integrates three neurologically-inspired systems: a phase-adaptive cognitive controller that dynamically decomposes tasks into exploration, approach, and completion phases with adaptive parameters; a dual-memory integration system implementing dual-modal memory that balances immediate context with selective long-term storage; and an uncertainty-modulated prediction regulator that continuously updates environmental predictions to modulate exploration behavior. Comprehensive experiments in MineDojo demonstrate that these human-inspired decision-making strategies enhance performance over recent techniques, with success rates improving by an average of 4.6\% across resource collection tasks while reducing task completion steps by an average of 7.1\%. Our approach bridges cognitive neuroscience and reinforcement learning, excelling in complex scenarios that require sustained exploration and strategic adaptation while demonstrating how neural-inspired models can solve key challenges in open-world AI systems.

Knowledge distillation (KD) is a widely applicable DNN (Deep Neural Network) compression technology, which aims to transfer knowledge from a pretrained teacher neural network to a target student neural network. In practice, an enormous teacher is extracted through the compression of a neural network to train a relatively compact student. In general, current KD approaches mostly minimize divergence between the intermediate layers or logits of the teacher network and student network. However, these methods ignore important features distribution in the teacher network space, which leads to the defect of current KD approaches in the fine-grained categorization task, e.g., metric learning. For this, we propose a novel approach that transfers features distribution in the hyperspherical space from the teacher network to the student network. Specifically, our approach facilitates the student to learn the distribution among samples in the teacher and reduces the intra-class variance. Extensive experimental evaluations on three well-known metric learning datasets show that our method can distill higher-level knowledge from the teacher network and achieve state-of-the-art performance.