Meta-Orchestration of Symbolic and Textual Agents for Personalized Decision-Making

Easy decisions deserve simple models, while difficult decisions may benefit from richer reasoning. Utility-based models offer efficient and interpretable predictions, whereas large language models (LLMs) incorporate contextual information at greater computational cost. We propose Orchestration of Symbolic and Textual Agents (OSTA), a hierarchical meta-reasoning framework that adaptively combines utility models and LLMs for personalized decision modeling. Its orchestrator comprises two complementary stages: (i) Soft Inference Path Preference (SIPP), which assigns soft preferences between utility-only and mixed utility–LLM inference path; and (ii) Path-Conditional Agent Reliability (PCAR), which estimates agents' performance reliability on each path. A value-of-computation-inspired rule combines these signals with a tunable cost penalty to jointly select the inference path and agent, enabling performance and efficiency modes. Across six real-world datasets spanning transportation, health, and socioeconomic decisions, OSTA's performance mode achieves the best or tied-best performance on five of them, while efficiency mode retains an comparable performance, with approximately 91% fewer estimated tokens and 94% lower estimated inference cost for selected outputs. Population-scale case studies further illustrate OSTA's potential for resource-efficient personalized decision modeling. The source code will be fully released upon paper acceptance.