8 papers
Structural Plasticity as Active Inference: A Biologically-Inspired Architecture for Homeostatic Control
Brennen A. Hill
Traditional neural networks, while powerful, rely on biologically implausible learning mechanisms such as global backpropagation. This paper introduces the Structurally Adaptive Pr…
The Geometry of Cortical Computation: Manifold Disentanglement and Predictive Dynamics in VCNet
Brennen A. Hill, Zhang Xinyu, Timothy Putra Prasetio
Despite their success, modern convolutional neural networks (CNNs) exhibit fundamental limitations, including data inefficiency, poor out-of-distribution generalization, and vulner…
Communicating Plans, Not Percepts: Scalable Multi-Agent Coordination with Embodied World Models
Brennen A. Hill, Mant Koh En Wei, Thangavel Jishnuanandh
Robust coordination is critical for effective decision-making in multi-agent systems, especially under partial observability. A central question in Multi-Agent Reinforcement Learni…
Generative World Models of Tasks: LLM-Driven Hierarchical Scaffolding for Embodied Agents
Brennen Hill
Recent advances in agent development have focused on scaling model size and raw interaction data, mirroring successes in large language models. However, for complex, long-horizon m…
The Physical Basis of Prediction: World Model Formation in Neural Organoids via an LLM-Generated Curriculum
Brennen Hill
The capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel fr…
Co-Evolving Complexity: An Adversarial Framework for Automatic MARL Curricula
Brennen Hill
The advancement of general-purpose intelligent agents is intrinsically linked to the environments in which they are trained. While scaling models and datasets has yielded remarkabl…