How do long-range interactions affect AI consensus?
AI-Driven Consensus: Modeling Multi-Agent Networks with Long-Range Interactions through path-Laplacian Matrices
This paper explores predicting "consensus" (agreement on a value) in multi-agent systems, especially when agents can influence each other indirectly (e.g., friend-of-a-friend). It uses "path-Laplacian" matrices to model these indirect influences and tests various machine learning models (LSTM, xLSTM, Transformer, XGBoost, ConvLSTM) to predict the final consensus value. Results show that considering indirect influences improves prediction accuracy. This is relevant to LLM-based multi-agent systems as it provides a framework for modeling complex agent interactions and predicting system behavior using deep learning models already used in natural language processing. Being able to predict consensus improves the robustness and efficiency of these multi-agent systems.