## 2. Introduction
### 2.1 Context and Motivation
Over the past decade, artificial intelligence has shifted from experimental prototypes to production-critical infrastructure powering search engines, recommendation systems, autonomous systems, and creative tools. Yet, despite advances in training algorithms, model architectures, and hardware acceleration, AI deployment practices remain fragmented and largely artisanal. Monolithic packaging—where architecture, parameters, preprocessing, and orchestration are tightly coupled—remains the default. This practice inhibits portability (requiring extensive engineering to move between runtimes), obstructs reproducibility (minor changes in preprocessing can alter results irreversibly), and inflates maintenance costs.
The challenge mirrors pre-standardization phases in other computing domains. In networking, the absence of a layered model led to vendor lock-in and poor interoperability until the adoption of the OSI model. In software engineering, microservice and plugin architectures revolutionized maintainability by enforcing well-defined module boundaries. In hardware, the Instruction Set Architecture (ISA) decoupled software ecosystems from physical processors, enabling innovation across both layers. AI systems have yet to reach a similar level of modular maturity.
### 2.2 Problem Definition
Current AI ecosystem fragmentation stems from:
1. **Lack of Interface Consistency** – Variations in file formats, parameter serialization, and model definitions.
2. **Coupled Lifecycle Stages** – Preprocessing and model execution often embedded in the same codebase.
3. **Limited Cross-Vendor Compatibility** – Inference optimizations tied to proprietary hardware or runtimes.
4. **Weak Reproducibility Guarantees** – Changes in any subcomponent can alter outputs without explicit documentation.
Partial solutions exist—ONNX enables intermediate representation exchange, MLIR supports compiler-level transformations, and Hugging Face provides community distribution of models—but none offer a **complete systems-level decomposition** with enforceable module boundaries.
### 2.3 Scope
This work proposes the **Standardized Modularization Framework for AI (SMF-AI)**, applicable to large language models (LLMs), computer vision (CV) systems, speech recognition, reinforcement learning agents, and multimodal architectures. SMF-AI explicitly defines how modules communicate, how their lifecycles are managed, and how they can be independently updated or replaced without destabilizing the system.
### 2.4 Contributions
The key contributions of this paper are:
– **Formal Specification** of SMF-AI’s five-module decomposition.
– **Schema Standards** for module manifests using JSON Schema, Protocol Buffers, and YAML descriptors.
– **Integrity and Versioning Mechanisms** ensuring cryptographic reproducibility.
– **Compatibility Wrappers** enabling legacy model integration.
– **Case Studies** demonstrating MAS+MPS reuse across ONNX Runtime, TensorRT, and llama.cpp.
The adoption of SMF-AI could establish a foundation for a vendor-neutral AI software stack, accelerate research reproducibility, and catalyze the emergence of AI component marketplaces. This paper serves as the reference point for both academic investigation and industrial standardization efforts.
