# Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models

arXiv: https://arxiv.org/abs/2501.14189 · HTML: https://mlsanity.com/paper/2501.14189/

## Key facts

- Cited by 3 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 17 of its 53 references that are in the map.
- First posted on arXiv 24 Jan 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, 17 of 17)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://mlsanity.com/paper/2305.12295/) (2023)
- [A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning](https://mlsanity.com/paper/2406.05804/) (2024)
- [Memory Augmented Large Language Models are Computationally Universal](https://mlsanity.com/paper/2301.04589/) (2023)
- [Active Preference Inference using Language Models and Probabilistic Reasoning](https://mlsanity.com/paper/2312.12009/) (2023)
- [Distributed Constraint Optimization Problems and Applications: A Survey](https://mlsanity.com/paper/1602.06347/) (2016)
- [Holy Grail 2.0: From Natural Language to Constraint Models](https://mlsanity.com/paper/2308.01589/) (2023)
- [MCP-Solver: Integrating Language Models with Constraint Programming Systems](https://mlsanity.com/paper/2501.00539/) (2024)
- [Combining Constraint Programming Reasoning with Large Language Model Predictions](https://mlsanity.com/paper/2407.13490/) (2024)
- [Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates](https://mlsanity.com/paper/2412.13471/) (2024)
- [Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs](https://mlsanity.com/paper/2002.12001/) (2020)
- [On Population-Based Algorithms for Distributed Constraint Optimization Problems](https://mlsanity.com/paper/2009.01625/) (2020)
- [Incorporating Human Flexibility through Reward Preferences in Human-AI Teaming](https://mlsanity.com/paper/2312.14292/) (2023)
- [A Privacy Preserving Collusion Secure DCOP Algorithm](https://mlsanity.com/paper/1905.09013/) (2019)
- [Neurosymbolic Value-Inspired AI (Why, What, and How)](https://mlsanity.com/paper/2312.09928/) (2023)

## Built on by (most cited first, 3 of 3)

- [Terrarium: Revisiting the Blackboard for Multi-Agent Safety, Privacy, and Security Studies](https://mlsanity.com/paper/2510.14312/) (2025)
- [MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security](https://mlsanity.com/paper/2604.07551/) (2026)
- [Symbolic Guidance for LLM Agents in Distributed Multiagent Coordination](https://mlsanity.com/paper/2609.31963/) (2026)

## Abstract (arXiv)

Distributed Constraint Optimization Problems (DCOPs) offer a powerful framework for multi-agent coordination but often rely on labor-intensive, manual problem construction. To address this, we introduce VL-DCOPs, a framework that takes advantage of large multimodal foundation models (LFMs) to automatically generate constraints from both visual and linguistic instructions. We then introduce a spectrum of agent archetypes for solving VL-DCOPs: from a neuro-symbolic agent that delegates some of the algorithmic decisions to an LFM, to a fully neural agent that depends entirely on an LFM for coordination. We evaluate these agent archetypes using state-of-the-art LLMs (large language models) and VLMs (vision language models) on three novel VL-DCOP tasks and compare their respective advantages and drawbacks. Lastly, we discuss how this work extends to broader frontier challenges in the DCOP literature.