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

arXiv: https://arxiv.org/abs/2508.08283 · HTML: https://mlsanity.com/paper/2508.08283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 10 of 10)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [A Survey of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2312.14925/) (2023)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [Overcoming Oscillations in Quantization-Aware Training](https://mlsanity.com/paper/2203.11086/) (2022)
- [LLM-MARS: Large Language Model for Behavior Tree Generation and NLP-enhanced Dialogue in Multi-Agent Robot Systems](https://mlsanity.com/paper/2312.09348/) (2023)
- [BTGenBot: Behavior Tree Generation for Robotic Tasks with Lightweight LLMs](https://mlsanity.com/paper/2403.12761/) (2024)
- [LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees](https://mlsanity.com/paper/2404.05134/) (2024)
- [LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs](https://mlsanity.com/paper/2410.11387/) (2024)
- [Behavior Trees in Action: A Study of Robotics Applications](https://mlsanity.com/paper/2010.06256/) (2020)

## Built on by (most cited first, 1 of 1)

- [Bridging the Knowledge Void: Inference-time Acquisition of Unfamiliar Programming Languages for Coding Tasks](https://mlsanity.com/paper/2602.06976/) (2026)

## Abstract (arXiv)

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.