A Dynamic Polyhedral Memory Framework for Generalist Multi-Modal AI

Recent advances in foundation models have pushed generalist AI forward, but existing architectures still face limitations in flexible adaptation and scalable memory across diverse tasks. This work presents a novel framework that combines dynamic polyhedral memory with transformer-based processing to build adaptive, multi-modal generalist agents. In this design, each memory unit is modeled as a convex polytope, creating a non-Euclidean latent space where reasoning occurs through Polyhedral Structural Attention (PSA). PSA computes geometric relevance scores over these polytopes, enabling efficient and context-aware memory traversal. Evaluations on tasks spanning vision-language navigation, tool manipulation, and symbolic reasoning show improvements in adaptation speed, interpretability, and memory efficiency compared to baselines such as PaLM-E and Gemini. These results suggest that geometric memory architectures provide a promising path toward scalable and interpretable generalist intelligence.