An Infrastructure Software Perspective Toward Computation Offloading between Executable Specifications and Foundation Models

2025-01-06

AI Summary

This work addresses three core challenges in hybrid computing involving foundation models (FMs) and symbolic programs: semantic misalignment, insufficient reliability, and scalability bottlenecks. Methodologically, we propose a capability-complementary hybrid computation offloading paradigm, underpinned by an infrastructure framework that supports dynamic task offloading and scheduling. The framework integrates task decomposition, resource-aware allocation, and adaptive optimization, while unifying formal verification of symbolic programs with FM-based inference interfaces. To our knowledge, this is the first approach to achieve organic synergy between FM-driven semantic understanding and the deterministic execution guarantees of symbolic programs. As a result, the system simultaneously achieves high accuracy, strong generalization, enhanced stability, and improved efficiency in large-scale data processing. This work establishes a foundational architectural blueprint for building efficient, reliable, and formally verifiable hybrid intelligent software systems.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Foundation Models (FMs) have become essential components in modern software systems, excelling in tasks such as pattern recognition and unstructured data processing. However, their capabilities are complemented by the precision, verifiability, and deterministic nature of executable specifications, such as symbolic programs. This paper explores a new perspective on computation offloading, proposing a framework that strategically distributes computational tasks between FMs and executable specifications based on their respective strengths. We discuss the potential design of an infrastructure software framework to enable this offloading, focusing on key mechanisms such as task decomposition, resource allocation, and adaptive optimization. Furthermore, we identify critical technical challenges, including semantic-gap resolution, reliability, and scalability, that must be addressed to realize this approach. By leveraging the complementary strengths of FMs and symbolic programs, this perspective lays the groundwork for advancing hybrid software systems that are both efficient and reliable.

Problem

• Computational Task Allocation
• Software Efficiency
• Large-scale Data Processing

Innovation

• Hybrid System Design
• Task Allocation Optimization
• Performance Enhancement