Emergence Transformer: Dynamical Temporal Attention Matters
Summary: arXiv:2604.19816v1 Announce Type: new
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 the 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.
Key Findings
- The Emergence Transformer utilizes dynamical temporal attention (DTA) to enhance the interaction of components within a network.
- DTA employs time-varying query, key, and value matrices to facilitate dynamic attention kernels.
- Neighbor-DTA promotes oscillatory coherence, while self-DTA optimally enhances coherence based on network structure.
- The architecture suggests new strategies for influencing social coherence, enhancing agreement or maintaining diversity.
- Application of DTA to Hopfield neural networks demonstrates potential for continual learning without experiencing catastrophic forgetting.
Implications for Future Research
This innovative approach to temporal attention sets the stage for further exploration in various fields, including:
- Quantum Systems: Investigating the role of DTA in enhancing coherence in quantum mechanics.
- Biophysical Models: Applying DTA to understand complex biological interactions and behaviors.
- Climate Systems: Exploring potential applications in climate modeling and prediction through improved data coherence.
- Social Dynamics: Developing strategies for influencing group behaviors and opinions in social networks.
As research progresses, the Emergence Transformer with dynamical temporal attention will likely pave the way for new methodologies and frameworks in understanding complex systems across diverse domains.