# Memory-Augmented State Machine Prompting: A Novel LLM Agent Framework for Real-Time Strategy Games

arXiv: https://arxiv.org/abs/2510.18395 · HTML: https://mlsanity.com/paper/2510.18395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Placed in

- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)

## Builds on (most cited first, 8 of 8)

- [Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory](https://mlsanity.com/paper/2305.17144/) (2023)
- [Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach](https://mlsanity.com/paper/2312.11865/) (2023)
- [LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities](https://mlsanity.com/paper/2504.16078/) (2025)
- [A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2403.10249/) (2024)
- [SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models](https://mlsanity.com/paper/2401.17749/) (2024)
- [TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game](https://mlsanity.com/paper/1809.07193/) (2018)
- [COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations](https://mlsanity.com/paper/2402.01786/) (2024)
- [LLM-PySC2: Starcraft II learning environment for Large Language Models](https://mlsanity.com/paper/2411.05348/) (2024)

## Built on by (most cited first, 2 of 2)

- [Evaluating Memory Structure in LLM Agents](https://mlsanity.com/paper/2602.11243/) (2026)
- [World Models for Policy Refinement in StarCraft II](https://mlsanity.com/paper/2602.14857/) (2026)

## Abstract (arXiv)

This paper proposes Memory-Augmented State Machine Prompting (MASMP), a novel framework for LLM agents in real-time strategy games. Addressing key challenges like hallucinations and fragmented decision-making in existing approaches, MASMP integrates state machine prompting with memory mechanisms to unify structured actions with long-term tactical coherence. The framework features: (1) a natural language-driven state machine architecture that guides LLMs to emulate finite state machines and behavior trees through prompts, and (2) a lightweight memory module preserving strategic variables (e.g., tactics, priority units) across decision cycles. Experiments in StarCraft II demonstrate MASMP's 60% win rate against the hardest built-in AI (Lv7), vastly outperforming baselines (0%). Case studies reveal the method retains LLMs' semantic comprehension while resolving the "Knowing-Doing Gap" through strict state-action mapping, achieving both interpretability and FSM-like reliability. This work establishes a new paradigm for combining neural and symbolic AI in complex decision-making.