Zero-Shot AGI via Multimodal Symmetry Learning: An Extension of the SHELL-AGENT Framework with Bidirectional Algebraic Alignment

We present a comprehensive training methodology for zero-shot artificial general intelligence (AGI) based on the SHELL-AGENT framework. Our approach learns symmetry generators from multimodal data-including video, language, audio, and motion capture-and factorizes human experience into a Lie algebra of independent symmetry dimensions. We introduce the T-tiling principle as the fundamental structure governing optimal transition dynamics across all scales of behavior. The resulting generator set enables zero-shot generalization to novel tasks through composition of learned symmetries. Crucially, we extend the framework to address alignment through a novel bidirectional algebraic refinement process. Rather than treating alignment prompts as external commands or using curated datasets of aligned behaviors, we extract the intrinsic Lie algebra structure of the prompt text itself. This prompt algebra acts as a structural template that constrains generator formation during multimodal learning. Simultaneously, the rich algebraic structure discovered from multimodal data refines and expands the prompt algebra, creating a shared grammatical foundation for cognition and values. The resulting agent exhibits zero-shot aligned behavior not because alignment was enforced, but because misaligned states are structurally unreachable under its generator algebra. We provide mathematical foundations, training procedures, and theoretical guarantees for this intrinsically aligned architecture.