X-MAS: Advancing Multi-Agent Systems with Heterogeneous LLMs

Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous applications, revolutionizing fields from content generation to complex problem-solving. Models like GPT, Gemini, and Qwen have become powerful tools. However, despite their strengths, single LLMs face inherent limitations, particularly when tackling multifaceted, complex, or real-world tasks. Issues such as factual inaccuracies (hallucinations) or difficulties in complex reasoning can constrain their performance.

In response to these challenges, the paradigm of LLM-based Multi-Agent Systems (MAS) has emerged as a promising avenue. MAS leverage the concept of collaboration, where multiple agents, each potentially specialized in a specific function or domain, work together to solve problems more effectively than a single, monolithic model could. This approach mimics human team dynamics, assigning different roles and tasks to individual agents to achieve a collective goal. MAS have shown success in diverse applications, including automated software development, mathematical problem-solving, and scientific discovery. Frameworks like ChatDev and MetaGPT utilize multiple coding agents to streamline software engineering, while systems like AI co-scientist employ MAS for enhanced research.

The Limitation of Homogeneous MAS

Despite the advancements facilitated by MAS, most existing frameworks commonly rely on a single LLM to power all agents within the system. While offering simplicity in design, this homogeneous approach inherits the limitations of the underlying LLM. If the chosen model has a specific weakness or tendency to err in a particular domain or function, this weakness is likely to propagate through the entire system, even with multiple agents collaborating. The collective intelligence of such a system is inherently capped by the capabilities of the single model it employs. For instance, if a single LLM consistently makes factual errors in a medical domain, a homogeneous MAS built on this model for medical diagnosis tasks might struggle to correct these fundamental mistakes through internal collaboration alone.

Introducing X-MAS: Heterogeneous LLM-Driven MAS

Inspired by the well-established advantages of diversity in human collective intelligence and machine learning model ensembles, the concept of heterogeneous LLM-driven MAS (termed X-MAS in the studied paper) proposes a departure from the homogeneous norm. X-MAS posits that by powering different agents within a MAS with diverse LLMs – models trained on different datasets, architectures, or by different teams – the system can harness a broader spectrum of capabilities and potentially mitigate the weaknesses of any single model. This approach aims to elevate the system's potential beyond the limit of individual models to leverage the collective strengths of a diverse set of LLMs.

The core idea is that different LLMs might excel in different areas. One model might be particularly strong at mathematical reasoning, another at creative text generation, a third at summarizing information, and yet another at evaluating factual accuracy. By strategically assigning these specialized or generally capable-but-diverse models to agents with corresponding functions, a heterogeneous MAS could potentially achieve higher overall performance and robustness.

X-MAS-Bench: A Comprehensive Evaluation Framework

To systematically explore the potential of heterogeneous MAS and provide guidance for selecting appropriate LLMs, the researchers developed X-MAS-Bench. This comprehensive testbed is specifically designed to evaluate the performance of various LLMs across different domains and MAS-related functions. Recognizing that agents in a MAS perform distinct roles, the benchmark assesses LLMs not just on general capabilities but on specific functions critical for agent interaction and task completion within a multi-agent setting.

X-MAS-Bench evaluates LLMs across five representative MAS-related functions:

Question-Answering (QA): Assessing the ability of an LLM agent to understand a query and provide a relevant and accurate answer.
Revise: Evaluating an agent's capability to review and improve upon existing text or outputs, correcting errors or enhancing quality.
Aggregation: Measuring an agent's skill in synthesizing information from multiple sources or perspectives into a cohesive and comprehensive response.
Planning: Assessing an agent's capacity to break down a complex problem into smaller steps or generate a sequence of actions to achieve a goal.
Evaluation: Examining an agent's ability to critique or grade the output of other agents or systems based on specific criteria.

These functions are evaluated across five common and critical domains:

Mathematics: Testing numerical reasoning and problem-solving skills.
Coding: Assessing code generation, debugging, and understanding capabilities.
Science: Evaluating knowledge and reasoning in scientific disciplines.
Medicine: Testing medical knowledge and diagnostic reasoning.
Finance: Assessing understanding of financial concepts and data.

The scale of X-MAS-Bench is substantial, involving the assessment of 27 different LLMs across these 5 functions and 5 domains, encompassing 21 distinct test sets. The evaluation process involved over 1.7 million individual assessments to generate a detailed performance profile for each LLM across the various function-domain combinations.

Key Findings from X-MAS-Bench

The extensive evaluation conducted using X-MAS-Bench yielded several critical insights that strongly support the rationale for heterogeneous MAS:

No Single LLM Excels Universally: The benchmark results clearly indicate that no single LLM achieves top performance across all evaluated functions and domains. A model that performs exceptionally well in mathematical reasoning might be mediocre in code generation or medical question-answering, and vice versa. This finding directly challenges the efficacy of homogeneous MAS, as relying on one model inevitably means sacrificing performance in areas where that model is weak.
Significant Performance Variation: A single LLM often exhibits significant performance variations depending on the specific function it is required to perform and the domain of the task. A model might be excellent at generating initial answers (QA) but poor at revising them or aggregating information from multiple sources.
Large Disparities Between LLMs: Within the same function and domain combination, different LLMs can show surprisingly large performance disparities. This highlights that for a specific task requiring a particular function in a particular domain (e.g., planning in a finance task), choosing the right LLM can have a dramatic impact on the agent's effectiveness.
Smaller LLMs Can Compete: Counter-intuitively, the study found instances where smaller LLMs outperformed much larger models on specific tasks. This suggests that model size is not the sole determinant of performance for specific functions or domains, and that specialized or more efficiently trained smaller models can be highly effective contributors to a MAS.

These findings from X-MAS-Bench provide empirical evidence that leveraging the diverse strengths of different LLMs is a viable and potentially superior approach for building more capable MAS. The detailed performance maps generated by the benchmark offer valuable guidance for practitioners and researchers seeking to select optimal models for specific agent roles and tasks.

X-MAS-Design: Transitioning to Heterogeneous MAS

Building upon the insights gleaned from X-MAS-Bench, the researchers explored the practical implications of transitioning from homogeneous to heterogeneous LLM-driven MAS. The core idea of X-MAS-Design is straightforward: given an existing MAS framework or designing a new one, instead of assigning the same LLM to all agents, assign different agents the LLMs that performed best for their specific function and domain according to the X-MAS-Bench results. This assignment process is rapid, potentially taking only seconds once the benchmark results are available.

To validate this approach, experiments were conducted using several existing MAS frameworks (LLM-Debate, AgentVerse, DyLAN) and a prototype MAS incorporating all five evaluated functions. These experiments were performed on test sets covering the same five domains but ensuring no sample overlap with the X-MAS-Bench evaluation sets to provide an unbiased assessment of the design principles.

The results of these experiments compellingly demonstrated the benefits of the heterogeneous configuration:

Consistent Improvements in Chatbot-Only MAS: In scenarios where agents primarily performed chatbot-like question-answering or interactive tasks, the heterogeneous MAS consistently outperformed their homogeneous counterparts. A notable example cited is an 8.4% performance gain observed on the MATH benchmark simply by switching from a single LLM to a selection of diverse LLMs based on their performance profiles.
Dramatic Gains in Mixed Scenarios: The advantages became even more pronounced in mixed MAS scenarios, particularly those involving complex reasoning. In a setup combining chatbot-like agents with dedicated reasoner agents, the heterogeneous MAS achieved remarkable performance boosts on challenging competition-level tasks. For instance, on the AIME-2024 benchmark, using heterogeneous LLMs improved the performance of the AgentVerse framework from 20% to 50%, and the DyLAN framework from 40% to 63%. These are significant improvements that demonstrate the power of combining models strong in different areas (e.g., models good at understanding prompts vs. models good at step-by-step reasoning).
Value of Increased Diversity: Further experiments revealed a monotonic relationship between the number of candidate LLMs considered for heterogeneous assignment and the resulting MAS performance. This finding reinforces the core hypothesis that greater diversity in the pool of available LLMs allows for better optimization and leads to improved collective system intelligence.

These results underscore the transformative potential of simply selecting and assigning LLMs based on their benchmarked capabilities for specific roles within a MAS. It suggests that significant performance gains can be achieved without necessarily redesigning the underlying MAS architecture, focusing instead on intelligently allocating the right tools (LLMs) to the right tasks (agent functions in specific domains).

Contributions

The paper highlights several key contributions:

X-MAS-Bench: The development and execution of a large-scale, comprehensive benchmark specifically designed for evaluating LLMs in the context of MAS functions and domains. This involved over 1.7 million evaluations of 27 LLMs across 25 distinct function-domain combinations, providing valuable data for LLM selection in MAS design.
X-MAS-Design: A demonstrated principle and empirical evidence showing that transitioning existing homogeneous MAS to heterogeneous configurations, guided by benchmark findings, consistently leads to improved performance.
Open Source Resources: The release of all data, code, and evaluation results associated with X-MAS-Bench and the experimental studies, facilitating further research and development in heterogeneous MAS.

The work builds upon existing research in two main areas: LLM-based MAS and the use of heterogeneous LLMs. Prior MAS frameworks have successfully shown the benefits of collaboration among agents, but predominantly within a homogeneous LLM setup. Meanwhile, other works have explored using multiple heterogeneous LLMs, often focusing on ensembling or discussion without a systematic evaluation of LLM capabilities specifically tailored for diverse MAS functions and domains. X-MAS distinguishes itself by systematically benchmarking LLM performance for MAS tasks and demonstrating how these findings can be directly applied to design or improve heterogeneous MAS with quantifiable performance gains across various domains and frameworks.

Conclusion and Future Directions

The research presented on X-MAS provides compelling evidence that leveraging the collective intelligence of diverse LLMs is a powerful strategy for enhancing the capabilities of multi-agent systems. The X-MAS-Bench offers a vital resource for understanding the strengths and weaknesses of different LLMs across various MAS-related tasks and domains. The X-MAS-Design approach demonstrates that simple, informed LLM assignment based on these benchmarks can yield substantial performance improvements, particularly in complex problem-solving scenarios.

The success of heterogeneous MAS opens up exciting avenues for future research. This includes exploring more nuanced and dynamic strategies for selecting and integrating LLMs within MAS, potentially allowing agents to switch models based on the task at hand or the progress of the collaboration. Investigating the scalability and adaptability of heterogeneous MAS across a wider range of industries and increasingly complex real-world tasks will be crucial for realizing the full potential of this paradigm. The findings underscore the importance of moving beyond the single-LLM constraint in MAS development to build more capable, robust, and intelligent collaborative AI systems.

Source(s)

Research Paper on Heterogeneous LLM Multi-Agent Systems (X-MAS)

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