Project

Adaptive Graph Topologies for Multi-Agent Systems

Objective

This project proposes the development of AdaptiveGraphMAS, a reinforcement learning-driven framework for autonomous construction and optimization of multi-agent system (MAS) topologies. Inspired by the MasHost paradigm, this research aims to advance beyond semi-autonomous patterns by treating MAS topology design as a dynamic graph optimization problem, where both agent roles and inter-agent communication structures adapt in real-time to task requirements. 1. Background and Motivation 1.1 Current Limitations Large Language Model (LLM)-driven multi-agent systems have shown promise in complex problem-solving, but face critical limitations: Manual Design Bottlenecks: Current MAS architectures rely heavily on hand-crafted interaction patterns and predefined role assignments Static Topologies: Most systems employ fixed communication graphs that cannot adapt to varying task complexities Human Bias: Heuristic rules introduce designer assumptions that may not generalize across domains Semi-Autonomous Paradigms: Even "adaptive" systems require significant human intervention in structural decisions 1.2 Opportunity By formulating MAS topology construction as a reinforcement learning problem on dynamic graphs, we can achieve: True Autonomy: Systems that self-organize without human-defined interaction patterns Task Adaptivity: Topologies that reconfigure based on query characteristics Multi-Objective Optimization: Balancing accuracy, efficiency, and structural rationality 2. Research Objectives Primary Goals Develop an RL-based framework for autonomous graph topology generation in multi-agent systems Enable dynamic adaptation of both agent roles and communication structures during task execution Achieve multi-objective optimization balancing performance, computational efficiency, and topological rationality Key Research Questions How can we formulate MAS topology as a learnable graph structure optimization problem? What RL strategies effective

Outcome

1. Performance Gains 2. Novel RL Algorithm 3. POC - proof of concept 4. Patent 5. Publication