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

• URL: http://arxiv.org/abs/2508.08283v1
• Date: Fri, 01 Aug 2025 13:10:29 GMT
• Title: MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language
• Authors: Andres Garcia Rincon, Eliseo Ferrante,
• Abstract summary: MinionsLLM provides standardized interfaces for defining environments, agents, and behavioral primitives.<n>It integrates Large Language Models (LLMs) with Behavior Trees (BTs) and Formal Grammars to enable natural language control of multi-agent systems.
• Score: 2.4171019220503402
• License: http://creativecommons.org/licenses/by/4.0/
• 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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