Convergence-Based Architectures: A Research Monograph on Multi-Perspective Representation Learning through Latent Agreement

Anthony Aseervatham

Convergence-Based Architectures: A New Computational Paradigm for Multi-Perspective Representation Learning This research monograph introduces Convergence-Based Architectures (CBA), a general computational paradigm in which representations emerge through latent agreement among multiple independent perspectives rather than solely through additive composition, concatenation, or sequential transformation. Unlike conventional neural architectures that progressively accumulate information, Convergence-Based Architectures explicitly preserve independent latent representations before allowing them to interact through convergence operators that discover an emergent representation reflecting mutual consistency. The monograph develops the conceptual foundations, mathematical framework, architectural design principles, and research landscape for convergence-based learning. It introduces latent agreement as a computational primitive, formalizes convergence operators in an implementation-independent framework, and discusses their relationship to existing neural paradigms including Convolutional Neural Networks, Transformers, Mixture-of-Experts, Energy-Based Models, and Diffusion Models. Initial proof-of-concept investigations spanning synthetic representation learning, financial time-series analysis, electrocardiography, and language modeling demonstrate the feasibility of the convergence principle across multiple domains. Rather than presenting a finalized theory, this work establishes Convergence-Based Architectures as an open research direction and outlines future opportunities in theory, scaling, multimodal learning, scientific reasoning, and next-generation AI systems.