SIGMAS: Second-Order Interaction-based Grouping for Overlapping Multi-Agent Swarms

2026-02-22

AI Summary

This work addresses the problem of inferring overlapping multi-agent group structures from trajectory data in unsupervised settings by proposing a novel self-supervised framework. It formalizes, for the first time, the task of overlapping group prediction through modeling second-order interactions—defined as similarities in interaction patterns among agents—and introduces a learnable gating mechanism to jointly optimize representations of both individual and group dynamics. In contrast to conventional approaches that rely solely on first-order interactions, the proposed framework significantly improves the accuracy and robustness of recovering latent group structures in complex, dynamically overlapping scenarios. This advancement establishes a new benchmark and modeling paradigm for understanding multi-agent group organization.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Swarming systems, such as drone fleets and robotic teams, exhibit complex dynamics driven by both individual behaviors and emergent group-level interactions. Unlike traditional multi-agent domains such as pedestrian crowds or traffic systems, swarms typically consist of a few large groups with inherent and persistent memberships, making group identification essential for understanding fine-grained behavior. We introduce the novel task of group prediction in overlapping multi-agent swarms, where latent group structures must be inferred directly from agent trajectories without ground-truth supervision. To address this challenge, we propose SIGMAS (Second-order Interaction-based Grouping for Multi-Agent Swarms), a self-supervised framework that goes beyond direct pairwise interactions and model second-order interaction across agents. By capturing how similarly agents interact with others, SIGMAS enables robust group inference and adaptively balances individual and collective dynamics through a learnable gating mechanism for joint reasoning. Experiments across diverse synthetic swarm scenarios demonstrate that SIGMAS accurately recovers latent group structures and remains robust under simultaneously overlapping swarm dynamics, establishing both a new benchmark task and a principled modeling framework for swarm understanding.

Problem

• group prediction
• overlapping multi-agent swarms
• latent group structures
• trajectory-based inference
• unsupervised grouping

Innovation

• second-order interaction
• self-supervised grouping
• overlapping multi-agent swarms
• latent group inference
• learnable gating mechanism