Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition
Summary: arXiv:2604.08708v1 Announce Type: cross
Abstract
While Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks, their intricate interactions introduce critical reliability challenges arising from communication dynamics and role dependencies. Existing Uncertainty Quantification methods, typically designed for single-turn outputs, fail to address the unique complexities of the MAS. Specifically, these methods struggle with three distinct challenges: the cascading uncertainty in multi-step reasoning, the variability of inter-agent communication paths, and the diversity of communication topologies.
Introduction
The advent of Large Language Models (LLMs) has revolutionized the landscape of artificial intelligence, particularly in the domain of Multi-Agent Systems (MAS). While these systems demonstrate superior performance compared to single-agent frameworks, they are not without their drawbacks. The interaction among agents can lead to unforeseen reliability issues, largely due to the complexities inherent in their communication dynamics.
Challenges in Multi-Agent Systems
Three main challenges hinder effective uncertainty quantification in MAS:
- Cascading Uncertainty: In multi-step reasoning tasks, the uncertainty from one step can propagate and amplify in subsequent steps.
- Variability of Communication Paths: The paths through which agents communicate can vary significantly, making it difficult to predict outcomes based on previous interactions.
- Diversity of Communication Topologies: Different tasks may require different agent arrangements and communication patterns, complicating the reliability assessment.
Introducing MATU
To address these challenges, we propose MATU, a novel framework designed to quantify uncertainty using tensor decomposition. Unlike traditional methods that focus solely on final outputs, MATU analyzes entire reasoning trajectories. By representing these trajectories as embedding matrices, we can organize multiple execution runs into a higher-order tensor.
Methodology
The application of tensor decomposition allows us to disentangle and quantify various sources of uncertainty present in MAS. This approach not only provides a more nuanced understanding of reliability but also offers a comprehensive measure that can be generalized across diverse agent structures.
Experimental Validation
We conducted a series of comprehensive experiments to validate the effectiveness of MATU. Our results indicate that MATU successfully estimates both holistic and robust uncertainty across a wide range of tasks and communication topologies. This demonstrates the framework’s adaptability and reliability in practical applications.
Conclusion
In conclusion, MATU represents a significant advancement in the field of uncertainty quantification for multi-agent systems. By employing tensor decomposition, we are able to provide a more reliable and comprehensive measure of uncertainty, paving the way for enhanced performance in complex tasks. As LLM-based MAS continue to evolve, frameworks like MATU will be crucial in ensuring their robustness and reliability.