Title: SmartDS-Solver: Agentic AI for Vertical Domain Problem Solving in Data Science
Primary area: infrastructure, software libraries, hardware, systems, etc.
Keywords: Agentic AI, Auto ML, Hyperparameter Tuning, NLP, Code Generation from Natural Language, Planning with Language Models

Abstract: We present SmartDS-Solver, a hierarchical multi-agent framework for adaptive and cost-efficient automation of data science workflows. While LLM-based agents show potential, current multi-agent systems suffer from fragile task coherence, excessive reliance on prompt-based interactions, and a tendency toward task silos. Pursuing improved performance through multi-turn exchanges is often misguided, as accuracy gains are uncertain and frequent API calls further drive up token costs. SmartDS-Solver addresses these challenges through: (i) a domain-specific reasoning LLM, trained via structured methodological distillation and two-stage GRPO fine-tuning, to enable robust end-to-end code generation; and (ii) a hierarchical Agentic AI architecture with task-decoupled multi-agent collaboration, in which a meta-learning agent dynamically adjusts expert model decoding strategies based on feedback from the code agent—thereby unlocking the full potential of the domain reasoning LLM and improving both the success rate and quality of data-science code generation. Evaluated on 11 MLE-Bench tasks, SmartDS-Solver attains an 81.8% win rate over the AIDE+o1-preview baseline while substantially reducing computational overhead. In additional real-world task evaluations, our method—using only a 7B model—successfully generated 100% executable code, whereas AIDE+o1-preview failed on one task.