Agentic Insight Generation in VSM Simulations

arXiv:2604.12421v1 Announce Type: new Abstract: Extracting actionable insights from complex value stream map simulations can be challenging, time-consuming, and error-prone. Recent advances in large language models offer new avenues to support users with this task. While existing approaches excel at processing raw data to gain information, they are structurally unfit to pick up on subtle situational differences needed to distinguish similar data sources in this domain. To address this issue, we propose a decoupled, two-step agentic architecture. By separating orchestration from data analysis, the system leverages progressive data discovery infused with domain expert knowledge. This architecture allows the orchestration to intelligently select data sources and perform multi-hop reasoning across data structures while maintaining a slim internal context. Results from multiple state-of-the-art large language models demonstrate the framework's viability: with top-tier models achieving accuracies of up to 86% and demonstrating high robustness across evaluation runs.

Related

• OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning
• HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation
• Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
• Structure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning

Source: arXiv cs.CL | 2026-04-15