MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915441180409856 |
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| author | Rincon, Andres Garcia Ferrante, Eliseo |
| author_facet | Rincon, Andres Garcia Ferrante, Eliseo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language Rincon, Andres Garcia Ferrante, Eliseo Computation and Language Artificial Intelligence Machine Learning Multiagent Systems Robotics 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. |
| title | MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language |
| topic | Computation and Language Artificial Intelligence Machine Learning Multiagent Systems Robotics |
| url | https://arxiv.org/abs/2508.08283 |