Controlled Dynamics Attractor Transformer

# Controlled Dynamics Attractor Transformer

**arXiv ID:** 2606.15207
**Authors:** Cheng Zhang, Minnan Luo, Zesheng Yang, Ming Li, Yong-Jin Liu, Qinghua Zheng
**Published:** 2026-06-13T09:04:22Z
**Abstract:**
Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) frameworks map representations onto energy landscapes, offering interpretable retrieval mechanisms. However, their continuous-time inference dynamics lack the biological plausibility of classical Continuous Attractor Neural Networks (CANNs). To bridge this gap, we propose Controlled Dynamics Attractor Transformer (CDAT), which couples a mixture von Mises-Fisher (Mo-vMF) attention energy with a Hopfield refinement energy, while augmenting energy descent with a CANN-inspired excitation-inhibition modulation. CDAT instantiates a topology-constrained dynamical system whose couplings encode relational structure among tokens, thereby linking attractor-style dynamics to modern energy-based attention. We further provide a constructive dissipation analysis to formally establish their controlled inference dynamics. Benefiting from these robust and structured dynamics, CDAT achieves state-of-the-art performance across multiple benchmarks in graph anomaly detection and graph classification.

## Skill Description

This skill is generated from the arXiv paper: Controlled Dynamics Attractor Transformer (2606.15207).

## How to Use

[To be filled in by the user or by future automation]

## References

- [arXiv:2606.15207](http://arxiv.org/abs/2606.15207v1)