COMMA: A Communicative Multimodal Multi-Agent Benchmark

• URL: http://arxiv.org/abs/2410.07553v1
• Date: Thu, 10 Oct 2024 02:49:47 GMT
• Title: COMMA: A Communicative Multimodal Multi-Agent Benchmark
• Authors: Timothy Ossowski, Jixuan Chen, Danyal Maqbool, Zefan Cai, Tyler Bradshaw, Junjie Hu,
• Abstract summary: We introduce a novel benchmark designed to evaluate the collaborative performance of multimodal multi-agent systems through language communication. By testing both agent-agent and agent-human collaborations using open-source and closed-source models, our findings reveal surprising weaknesses in state-of-the-art models.
• Score: 7.831385481814481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advances of multi-modal agents built on large foundation models have largely overlooked their potential for language-based communication between agents in collaborative tasks. This oversight presents a critical gap in understanding their effectiveness in real-world deployments, particularly when communicating with humans. Existing agentic benchmarks fail to address key aspects of inter-agent communication and collaboration, particularly in scenarios where agents have unequal access to information and must work together to achieve tasks beyond the scope of individual capabilities. To fill this gap, we introduce a novel benchmark designed to evaluate the collaborative performance of multimodal multi-agent systems through language communication. Our benchmark features a variety of scenarios, providing a comprehensive evaluation across four key categories of agentic capability in a communicative collaboration setting. By testing both agent-agent and agent-human collaborations using open-source and closed-source models, our findings reveal surprising weaknesses in state-of-the-art models, including proprietary models like GPT-4o. These models struggle to outperform even a simple random agent baseline in agent-agent collaboration and only surpass the random baseline when a human is involved.

Related papers

• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems [15.159418172629701]
  Large Language Models (LLMs) are becoming increasingly powerful and capable of handling complex tasks. Compared to single agents, multi-agent systems have higher requirements for the collaboration capabilities of language models. We propose a benchmark, called BattleAgentBench, which defines seven sub-stages of three varying difficulty levels.
  arXiv  Detail & Related papers  (2024-08-28T17:43:55Z)
• Scaling Large-Language-Model-based Multi-Agent Collaboration [75.5241464256688]
  Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration. Inspired by the neural scaling law, this study investigates whether a similar principle applies to increasing agents in multi-agent collaboration.
  arXiv  Detail & Related papers  (2024-06-11T11:02:04Z)
• Multi-Agent Consensus Seeking via Large Language Models [6.922356864800498]
  Multi-agent systems driven by large language models (LLMs) have shown promising abilities for solving complex tasks in a collaborative manner. This work considers a fundamental problem in multi-agent collaboration: consensus seeking.
  arXiv  Detail & Related papers  (2023-10-31T03:37:11Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors [93.38830440346783]
  We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
  arXiv  Detail & Related papers  (2023-08-21T16:47:11Z)
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society [58.04479313658851]
  This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents. We propose a novel communicative agent framework named role-playing. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems.
  arXiv  Detail & Related papers  (2023-03-31T01:09:00Z)
• The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning [6.18778092044887]
  Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.
  arXiv  Detail & Related papers  (2020-08-06T12:48:08Z)
• Learning Individually Inferred Communication for Multi-Agent Cooperation [37.56115000150748]
  We propose Individually Inferred Communication (I2C) to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network. I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios.
  arXiv  Detail & Related papers  (2020-06-11T14:07:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.