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arXiv · 2508.08283

MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language

Andres Garcia Rincon·Eliseo Ferrante
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Abstract

This paper presents MinionsLLM, a novel framework that integrates Large Language Models (LLMs) with Behavior Trees (BTs) and Formal Grammars to enable natural language control of multi-agent systems within arbitrary, user-defined environments. MinionsLLM provides standardized interfaces for defining environments, agents, and behavioral primitives, and introduces two synthetic dataset generation methods (Method A and Method B) to fine-tune LLMs for improved syntactic validity and semantic task relevance. We validate our approach using Google's Gemma 3 model family at three parameter scales (1B, 4B, and 12B) and demonstrate substantial gains: Method B increases syntactic validity to 92.6% and achieves a mean task performance improvement of 33% over baseline. Notably, our experiments show that smaller models benefit most from fine-tuning, suggesting promising directions for deploying compact, locally hosted LLMs in resource-constrained multi-agent control scenarios. The framework and all resources are released open-source to support reproducibility and future research.

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Andres Garcia Rincon, Eliseo Ferrante. 2025-08-01. MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language. https://arxiv.org/abs/2508.08283

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