Compositional AI Beyond LLMs: System Implications of Neuro-Symbolic-Probabilistic Architectures

Large Language Models (LLMs) have driven remarkable progress in artificial intelligence (AI), but their rapid growth faces challenges of unsustainable computation, limited robustness, and poor explainability. Compositional AI, which integrates LLMs with symbolic reasoning and probabilistic inference, has emerged as a promising paradigm to enable interpretability, robustness, trustworthiness, and data-efficient learning. Recent neuro-symbolic-probabilistic systems demonstrate strong potential in agentic applications, advancing reasoning and cognitive capabilities toward human-like intelligence.