This paper introduces a novel world model that leverages Vector Symbolic Architecture (VSA) principles, specifically Fourier Holographic Reduced Representation (FHRR), to address the limitations of unstructured neural network-based models. By encoding states and actions as high-dimensional complex vectors and modeling transitions with element-wise multiplication, the proposed model achieves superior generalization, sample efficiency, and interpretability.

This paper introduces "symmetrical reality," a new paradigm for human-machine interaction that moves beyond human-centric views. It proposes a framework where humans and advanced AI entities coexist symbiotically, capable of symmetrically perceiving and interacting across both physical and virtual worlds.

This paper introduces an active inference computational framework for empathy in AI agents, enabling explicit perspective-taking through a self-other model transformation. It demonstrates that empathic perspective-taking can induce robust cooperation in strategic dilemmas like the Iterated Prisoner's Dilemma, highlighting empathy as a structural prior for socially aligned AI.

This paper introduces FedGIN, a federated learning framework that uses augmentation-driven generalization to enable robust medical image segmentation across different imaging modalities (CT and MRI) without sharing sensitive patient data. It demonstrates that spatial-domain augmentations, specifically Global Intensity Nonlinear (GIN), significantly improve segmentation accuracy in cross-modmodality federated settings.

This paper proposes a novel scheduling and analysis method for Directed Acyclic Graph (DAG)-structured GPU tasks to enhance real-time predictability. It introduces techniques like parallelism scaling and node segmentation to reduce task execution time and provide a safe makespan bound without relying on kernel priorities.