arXiv:2606.12691v1 Announce Type: cross Abstract: Auto-regressive models have emerged as powerful tools for sequential data, from language to video. Understanding how and why these models learn latent representations remains an open theoretical question. In this work, we demonstrate that when trained by empirical risk minimization on data from partially observed linear dynamical systems, two-layer linear auto-regressive models naturally learn to approximate Kalman filtering. In particular, we show that the learned hidden representation coincides, up to a similarity transformation, with the sta
This paper provides foundational theoretical understanding for how common auto-regressive models learn complex internal representations, emerging from the rapid advancement and widespread adoption of these models in AI.
Understanding the theoretical underpinnings of auto-regressive models' latent state learning ability is crucial for developing more robust, interpretable, and efficient AI systems, impacting their practical application across various domains.
This work begins to demystify the 'black box' nature of deep learning models, providing a theoretical framework that could lead to more predictable and controllable AI system design, moving beyond purely empirical approaches.
- · AI researchers
- · Machine learning engineers
- · Developers of AI safety and interpretability tools
- · Industries relying on sequential data analysis
- · Companies with opaque, uninterpretable AI models
Improved understanding leads to more targeted development of auto-regressive models.
This foundational knowledge enables the creation of more trustworthy and explainable AI applications in sensitive areas.
The ability to formally verify aspects of learned representations could accelerate AI adoption where regulatory hurdles or trust issues currently exist.
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Read at arXiv cs.AI