Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems

Abstract

Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. However, the topology of these systems-how agents in MASs should be configured, connected, and coordinated-remains largely unexplored. In this position paper, we call for a paradigm shift toward topology-aware MASs that explicitly model and dynamically optimize the structure of inter-agent interactions.

We identify three fundamental components-agents, communication links, and overall topology-that collectively determine the system's adaptability, efficiency, robustness, and fairness. To operationalize this vision, we introduce a systematic three-stage framework: 1) agent selection, 2) structure profiling, and 3) topology synthesis. This framework not only provides a principled foundation for designing MASs but also opens new research frontiers across language modeling, reinforcement learning, graph learning, and generative modeling to ultimately unleash their full potential in complex real-world applications.

Why Topology Matters

In multi-agent systems, inter-agent communication determines how information flows, how tasks are decomposed, and how consensus is reached. As task complexity increases and the number of agents scales up, communication costs often rise quadratically.

Efficiency Challenge

Naïve scaling without adaptive structural design results in redundant communication, higher GPU memory and token costs, and longer response latency.

Performance Impact

Performance can vary by up to 10% across different topological structures. The optimal topology is task-dependent.

Safety Implications

Poorly designed topology may lead to misinformation propagation, biased reasoning, or generation of harmful content.

Three-Stage Framework

We propose a unified framework that decomposes topological structure optimization into three stages:

Stage 1: Agent Selection

Goal: Select a subset of agents best suited to collaborate on the task

Considerations: Skill specialization, role diversity, past performance, and cost-utility trade-offs

Approach: Multi-armed bandits, temporal graph neural networks, and submodular optimization

Stage 2: Structure Profiling

Goal: Identify macro-level communication patterns (chain, tree, star, graph)

Considerations: Task coordination requirements, communication budget, and structural properties

Approach: Task-to-structure classifiers with cost-aware reweighting

Stage 3: Topology Synthesis

Goal: Synthesize fine-grained communication topology governing information flow

Considerations: Edge directions, weights, hierarchy, and role-specific positioning

Approach: Topology pruning, counterfactual reasoning, and generative models

Four Key Positions

P1: Resource Constraint

Topological structure directly determines the resource required for agent collaboration. Poorly designed topologies lead to redundant communication and computational bottlenecks.

P2: Task Performance

Topology serves as a critical factor that can either amplify or hinder task performance. Different applications favor different agent structures.

P3: Robustness

System robustness critically depends on topological structure, which shapes tolerance to failures and adversarial perturbations.

P4: Fairness

Topology significantly influences fairness of resource allocation and decision-making. Balanced topologies enable diverse roles to participate equitably.

Key Challenges

Task-Dependent Optimization

Universal topology design is difficult as structural preferences vary across domains and are hard to generalize.

Combinatorial Complexity

The design space grows exponentially with scale, rendering exhaustive optimization infeasible.

Evaluation Cost

Evaluating topologies requires end-to-end execution under real task dynamics, which is computationally expensive.

Research Directions

Open Problems and Future Work

Agent Selection: Topology-aware perspectives for structured reasoning, efficient routing, selective activation, and dynamic composition
Structure Profiling: Few-shot structure classifiers with uncertainty quantification and continual learning from deployment logs
Topology Synthesis: Measuring fine-grained, task-specific influence of communication links and exploring generative models for topology generation
Benchmarks: Topology-aware benchmarks that capture coordination efficiency, communication redundancy, and error propagation
Robustness: Studying redundancy requirements to resist agent failures and adversarial attacks
Fairness: Ensuring equitable participation and preventing structural biases in decision-making

Broader Impact

Topology-aware MASs can benefit numerous real-world applications:

Customer Support

Reduce response latency and minimize redundant information routing in multi-turn dialogues

Collaborative Coding

Better coordination among agents with distinct roles (planner, coder, tester)

Scientific Discovery

Optimize how domain-specific agents interact when solving open-ended scientific queries

BibTeX

@article{yang2025topological,
  title={Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems},
  author={Yang, Jiaxi and Zhang, Mengqi and Jin, Yiqiao and Chen, Hao and Wen, Qingsong and Lin, Lu and He, Yi and Kumar, Srijan and Xu, Weijie and Evans, James and Wang, Jindong},
  journal={arXiv:2505.22467},
  year={2025}
}

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