Toward Tool-Augmented Semantic Computing: Integrating Large Language Models with Data Grounding and Optimization for Real-World Problem Solving
TL;DR
This paper proposes a framework for tool-augmented semantic computing, in which LLMs are combined with external data sources and specialized algorithmic components, and demonstrates how tasks such as point-of-interest selection, coordinate retrieval, and route optimization can be decomposed into modular processing steps.
Abstract
Large Language Models (LLMs) have significantly advanced semantic computing by enabling systems to interpret and generate natural language with high flexibility. However, they remain limited in solving real-world problems that require precise computation, reliable data access, and structured optimization. Tasks such as route planning, geospatial reasoning, and combinatorial optimization highlight these shortcomings, particularly in terms of factual grounding and solution feasibility. This paper proposes a framework for tool-augmented semantic computing, in which LLMs are combined with external data sources and specialized algorithmic components. The framework consists of a semantic layer for interpreting user intent, a tool layer for data access and computation, and an orchestration layer coordinating their interaction. Using travel planning as a representative use case, we demonstrate how tasks such as point-of-interest selection, coordinate retrieval, and route optimization can be decomposed into modular processing steps. In particular, route sequencing is formulated as a variant of the Traveling Salesman Problem, requiring dedicated optimization beyond the capabilities of LLMs. The results show that integrating semantic reasoning with data grounding and algorithmic optimization enables reliable solutions to complex real-world problems, highlighting the potential of hybrid AI systems for future semantic computing applications.