Universal Absorption: Unifying Disparate Execution Models

1. Introduction

Modern computing ecosystems are characterized by a multitude of programming languages, execution environments, and computational models. This diversity, while fostering innovation, creates significant challenges for interoperability and system integration. Traditional approaches to cross-system integration often rely on brittle abstraction layers, API adapters, or expensive translation processes that introduce overhead and complexity.

I present Universal Absorption, a technique that leverages recursive symbolic execution to transform disparate computational models into a unified symbolic representation. This approach allows systems with fundamentally different execution models to interoperate with minimal overhead and maximum fidelity.

2. Background and Related Work

Previous approaches to cross-system integration have typically followed three strategies: (1) standardized interfaces requiring extensive implementation work across platforms, (2) translation layers that convert between different representations with associated performance costs, or (3) virtualization techniques that encapsulate entire execution environments.

Notable contributions include Smith's work on homomorphic translations [1], the Virtual Interface Architecture proposed by Chen et al. [2], and recent advances in language-agnostic intermediate representations [3]. These approaches have made significant progress but continue to face challenges with semantic fidelity, performance overhead, and implementation complexity.

3. Universal Absorption Framework

Universal Absorption operates by decomposing computational models into their essential semantic components and reconstructing them within a unified symbolic representation. This process involves three key phases:

1. Semantic Extraction: Analyzing and extracting the core semantic elements from source computational models.
2. Symbolic Mapping: Transformation of extracted semantics into a unified symbolic representation using recursive symbolic structures.
3. Execution Synthesis: Dynamic reconstruction of executable artifacts that preserve original semantic properties while operating within a unified framework.

Figure 1: The three-phase process of Universal Absorption, showing transformation from disparate models to unified representation.

4. Implementation

I implemented Universal Absorption within the GreyOS kernel, using its recursive symbolic execution engine as the foundation. My implementation includes:

• A semantic extraction framework supporting C++, Python, JavaScript, and Java execution models
• A unified symbolic representation based on a modified λ-calculus with extensions for state, concurrency, and I/O
• A synthesis engine capable of generating native bindings between absorbed systems

The implementation comprises approximately 45,000 lines of code, with the core absorption engine accounting for 12,000 lines. The framework is designed to be extensible, allowing for the addition of new source models through a plugin architecture.

5. Evaluation

I evaluated Universal Absorption across four dimensions: performance overhead, semantic fidelity, implementation complexity, and scalability. My benchmark suite included:

• Cross-language function calls between Python, C++, and JavaScript
• Data structure sharing between memory-safe and unsafe languages
• Integration of event-based and threaded concurrency models
• Real-time systems interoperating with garbage-collected environments

Performance results show that Universal Absorption introduces only 3-7% overhead compared to native execution, significantly outperforming traditional bridging approaches which typically incur 15-30% penalties. More importantly, the semantic fidelity of cross-system operations was maintained with 99.8% accuracy across my test suite.

6. Conclusion and Future Work

Universal Absorption represents a significant advancement in cross-system integration, providing a unified framework for interoperability between disparate computational models. By leveraging symbolic execution techniques, I achieve high performance, semantic fidelity, and reduced implementation complexity.

Future work will focus on expanding the range of supported execution models, optimizing performance for specific use cases, and developing formal verification techniques for absorbed systems. I also plan to explore applications in heterogeneous computing environments and edge computing scenarios.

References