STAR: Steelmaking Task-Aware Routing for Multi-Agent LLM Expert Systems

Steelmaking involves long, tightly coupled process chains and specialized domain knowledge, making it difficult in practice for a single general-purpose LLM to consistently align engineers’ queries with the correct process stage. This paper presents STAR, an industry-oriented multi-stage process-domain router for steel metallurgy, and provides an integration blueprint that maps routing labels to domain-specific prompting and retrieval scopes in a router-plus-agents architecture. We construct a quality-controlled metallurgical corpus from textbooks, manuals, and papers via OCR and multi-dimensional text-quality scoring. Based on this corpus, we build an LLM-assisted pipeline to synthesize query–domain pairs for eight fine-grained process domains under domain definitions/keywords and format constraints, and index all queries in a shared embedding space with FAISS. We design a three-stage router: (1) a lightweight filter using chit-chat rules and a nearest-neighbor distance threshold to separate steel-related queries from general ones, (2) a kNN label-voting router whose confidence is derived from the Top-k neighbor label concentration, and (3) an LLM-based refinement step for low-confidence cases with safe fallback. Experiments on 3136 steel-domain queries and approximately 2000 general queries show that STAR achieves 0.921 Top-1 accuracy and 0.899 macro-F1 on 8-way fine-grained steel-domain routing, and achieves a steel-query recall of 0.999 for steel-versus-general filtering (queries routed to general_llm in deployment). In this work, we primarily evaluate routing quality and efficiency; end-to-end answer quality evaluation of downstream agents is left for future work.