Emergence Transformer: Dynamical Temporal Attention Matters

2026-04-17

AI Summary

Traditional Transformers struggle to model dynamic long-range dependencies in temporal systems and fail to effectively regulate emergent coherence in complex systems. This work proposes a Dynamic Temporal Attention (DTA) mechanism, which for the first time integrates time-varying query, key, and value matrices into the Transformer architecture, enabling network components to interact with their own and neighboring units’ historical states. The approach reveals distinct regulatory roles of self-attention and neighborhood attention in modulating oscillatory coherence, achieves continual learning without catastrophic forgetting in Hopfield networks, and successfully governs social consensus behaviors. By doing so, it establishes a general paradigm for controlling coherence in complex dynamical systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The Transformer, a breakthrough architecture in artificial intelligence, owes its success to the attention mechanism, which utilizes long-range interactions in sequential data, enabling the emergent coherence between large language models (LLMs) and data distributions. However, temporal attention, that is, different forms of long-range interactions in temporal sequences, has rarely been explored in emergence phenomenon of complex systems including oscillatory coherence in quantum, biophysical, or climate systems. Here, by designing dynamical temporal attention (DTA) with time-varying query, key, and value matrices, we propose an Emergence Transformer. This architecture allows each component to interact with its own or its neighbors' past states through dynamical attention kernels, thereby enabling the promotion and/or suppression of the emergent coherence of components. Interestingly, we uncover that neighbor-DTA consistently promotes oscillatory coherence, whereas self-DTA exhibits an optimal attention weight for coherence enhancement, owing to its non-monotonic dependence on network structure. Practically, we demonstrate how DTA reshapes social coherence, suggesting strategies to either enhance agreement or preserve plurality. We further apply DTA to the paradigmatic Hopfield neural network, achieving emergent continual learning without catastrophic forgetting. Together, these results lay a foundation and provide an immediate paradigm for modulating emergence phenomenon in networked dynamics only using DTA.

Problem

• emergence
• temporal attention
• oscillatory coherence
• complex systems
• long-range interactions

Innovation

• Dynamical Temporal Attention
• Emergence Transformer
• Oscillatory Coherence
• Continual Learning
• Networked Dynamics