Task-Optimized Multi-Agent Communication Protocols with G-Designer. New AI research paper (see below).

G-Designer introduces a framework for dynamically designing communication topologies in LLM-based multi-agent systems using Graph Neural Networks (GNNs) and Variational Graph Auto-Encoders (VGAEs).

The system models the multi-agent system as a directed graph, where each agent is a node characterized by its language model, role, state, and available plugins. The encoder maps the agents' features to a latent space, while the decoder reconstructs an optimized, sparse communication graph tailored to the task at hand.

The primary goal of G-Designer is to enhance task-specific communication efficiency, minimize computational and token overhead, and ensure robustness against adversarial attacks.

Optimization of the communication graph is driven by the Multi-Agent Communication Protocol (MACP) function, balancing utility maximization, communication complexity reduction, and robustness.

The framework was tested on various benchmarks, demonstrating significant improvements in performance, efficiency, and resilience compared to static topologies. G-Designer’s ability to dynamically adapt the communication structure based on the task enables scalable, efficient, and robust multi-agent collaboration for diverse AI applications.

All rights w/ authors:
G-Designer: Architecting Multi-agent Communication
Topologies via Graph Neural Networks
https://arxiv.org/pdf/2410.11782

00:00 Task complexity defines the topology
03:37 Multi-agents as graphs
05:44 Multi Agent comm protocol MACP
07:20 Is complexity LLM specific
13:14 The topology structure
16:37 Communication pipeline
19:00 Optimization of protocol
22:55 MAC G Designer
28:20 The Anchor topology A
30:14 Design Comm topology with VGAE
38:27 optimize MAC G Designer
39:30 Workflow of MAC G Designer
41:50 Official AI Paper
44:45 You should consider




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