arXiv:2605.29351v1 Announce Type: new Abstract: We study minimal attention-only transformers under all-token corruption and show they admit a two-stage empirical Bayes interpretation. A single attention step computes a kernel-weighted posterior mean with respect to the empirical distribution defined by the context. Depth refines this distribution through particle dynamics (Stage 1), while a long-range skip-connection carries the noisy input as a query for posterior inference (Stage 2), revealing distinct statistical roles for depth and attention residuals. The framework isolates a minimal sett
This research provides a deeper, principled understanding of attention mechanisms in transformers, a crucial component of modern AI models, refining the theoretical underpinnings of current breakthroughs.
A more profound theoretical understanding of attention-only transformers can lead to more efficient architectures, better performance, and potentially new modalities of AI development.
The interpretation of attention and depth in transformers shifts from empirical observations to a two-stage empirical Bayes framework, offering new avenues for model design and optimization.
- · AI researchers
- · Deep learning framework developers
- · Companies building advanced AI models
- · Researchers relying solely on empirical trial-and-error
- · Less theoretically grounded AI development approaches
Improved understanding of transformer behavior and potential for more robust model design.
Development of next-generation transformer architectures that leverage this two-stage empirical Bayes view.
Acceleration in the efficiency and computational performance of AI models, impacting computational resource requirements.
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Read at arXiv cs.LG