Beyond Monolithic Models: Symbolic Seams for Composable Neuro-Symbolic Architectures
This addresses the need for more transparent and extensible AI systems, particularly relevant for software architecture and AI development, though it is incremental as it builds on existing neuro-symbolic concepts.
The paper tackles the problem of monolithic AI models lacking transparency and adaptability by proposing symbolic seams as architectural breakpoints for composable neuro-symbolic designs, aiming to combine data-driven adaptability with verifiable symbolic constraints.
Current Artificial Intelligence (AI) systems are frequently built around monolithic models that entangle perception, reasoning, and decision-making, a design that often conflicts with established software architecture principles. Large Language Models (LLMs) amplify this tendency, offering scale but limited transparency and adaptability. To address this, we argue for composability as a guiding principle that treats AI as a living architecture rather than a fixed artifact. We introduce symbolic seams: explicit architectural breakpoints where a system commits to inspectable, typed boundary objects, versioned constraint bundles, and decision traces. We describe how seams enable a composable neuro-symbolic design that combines the data-driven adaptability of learned components with the verifiability of explicit symbolic constraints -- combining strengths neither paradigm achieves alone. By treating AI systems as assemblies of interchangeable parts rather than indivisible wholes, we outline a direction for intelligent systems that are extensible, transparent, and amenable to principled evolution.