# LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs

arXiv: https://arxiv.org/abs/2410.11387 · HTML: https://mlsanity.com/paper/2410.11387/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 3 of 3)

- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023)
- [Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation](https://mlsanity.com/paper/2309.17234/) (2023)

## Built on by (most cited first, 12 of 12)

- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [Safety Guardrails for LLM-Enabled Robots](https://mlsanity.com/paper/2503.07885/) (2025)
- [Large Language Models for Multi-Robot Systems: A Survey](https://mlsanity.com/paper/2502.03814/) (2025)
- [Multi-Agent Systems Powered by Large Language Models: Applications in Swarm Intelligence](https://mlsanity.com/paper/2503.03800/) (2025)
- [GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models](https://mlsanity.com/paper/2503.23875/) (2025)
- [SwarmWorld: Stigmergic technological evolution in societies of language-model agents](https://mlsanity.com/paper/2608.26081/) (2026)
- [Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy](https://mlsanity.com/paper/2510.04774/) (2025)
- [RACAS: Controlling Diverse Robots With a Single Agentic System](https://mlsanity.com/paper/2603.05621/) (2026)
- [MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language](https://mlsanity.com/paper/2508.08283/) (2025)
- [Beyond Scaling: Agents Are Heading to the Edge](https://mlsanity.com/paper/2605.18535/) (2026)
- [PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs](https://mlsanity.com/paper/2606.00104/) (2026)
- [Waggle: Learning One Anonymous Local Law for Self-Organizing LLM Swarms](https://mlsanity.com/paper/2609.34136/) (2026)

## Abstract (arXiv)

Robot swarms are composed of many simple robots that communicate and collaborate to fulfill complex tasks. Robot controllers usually need to be specified by experts on a case-by-case basis via programming code. This process is time-consuming, prone to errors, and unable to take into account all situations that may be encountered during deployment. On the other hand, recent Large Language Models (LLMs) have demonstrated reasoning and planning capabilities, introduced new ways to interact with and program machines, and incorporate both domain-specific and commonsense knowledge. Hence, we propose to address the aforementioned challenges by integrating LLMs with robot swarms and show the potential in proofs of concept (showcases). For this integration, we explore two approaches. The first approach is 'indirect integration,' where LLMs are used to synthesize and validate the robot controllers. This approach may reduce development time and human error before deployment. Moreover, during deployment, it could be used for on-the-fly creation of new robot behaviors. The second approach is 'direct integration,' where each robot locally executes a separate LLM instance during deployment for robot-robot collaboration and human-swarm interaction. These local LLM instances enable each robot to reason, plan, and collaborate using natural language, as demonstrated in our showcases where the robots are able to detect a variety of anomalies, without prior information about the nature of these anomalies. To enable further research on our mainly conceptual contribution, we release the software and videos for our LLM2Swarm system: https://github.com/Pold87/LLM2Swarm.