Meta-Multimodal Foundation Models: A General Framework for Generating, Configuring, and Evolving Multimodal Model Mechanisms

Multimodal foundation models integrate information from multiple modalities, including text, images, audio, video, and other structured or sensory data. Existing research has investigated multimodal representation learning, cross-modal alignment, fusion architecture search, modality routing, and the construction of general-purpose multimodal assistants. However, a broader question remains: can a multimodal foundation model generate and adapt the mechanisms through which other multimodal foundation models are constructed and operated, rather than merely processing multimodal inputs or generating multimodal outputs?

This paper proposes Meta-Multimodal Foundation Models (Meta-MMFMs) as a general conceptual framework for investigating this question. The proposed framework introduces a meta-level system that can generate, select, combine, modify, and evaluate multimodal model mechanisms according to task requirements, user preferences, available resources, and observed performance. Its potential search space includes modality selection, modality-specific encoders and decoders, cross-modal interaction structures, representation alignment, fusion mechanisms, information-routing policies, generation processes, and model-level coordination. The framework distinguishes ordinary multimodal generation from the generation of mechanisms that govern multimodal generation itself.

A formal problem formulation is introduced in which a meta-model selects or constructs a task-dependent multimodal model configuration under performance, computational, latency, reliability, and compatibility constraints. The paper further develops a hierarchical architecture, a mechanism-generation process, an adaptive optimization objective, illustrative use cases, and an experimental evaluation protocol. Unlike approaches that search only within a predefined set of fusion operations, the proposed framework considers the broader possibility of generating and revising multiple interconnected components of a multimodal foundation model. Nevertheless, its relationship to neural architecture search, hypernetworks, meta-learning, model composition, and dynamic multimodal systems must be established through systematic comparisons. This paper presents a research framework and testable hypotheses, not a claim of demonstrated empirical superiority.