Dynamic Transformer Architecture (DTA)
Dynamic Transformer Architecture (DTA) is a theoretical and currently untested research framework proposing a way to add a mathematically regulated internal state to standard Transformer models. Unlike classical Transformers—which reset their internal dynamics at every token—DTA introduces a Dynamic State Path (DSP) that maintains a persistent state vector across the entire sequence. This state evolves using measurable internal signals (coherence, alignment, noise, continuity, and substrate stability) and is reintroduced into the model through a lightweight Integration Path.
The goal of DTA is to explore whether stability-regulated recurrence can improve long-horizon reasoning, reduce drift, and provide more consistent behavior under noise or extended workloads.
DTA has not yet been implemented or validated.
It is presented as a blueprint for experimentation, testing, and community-driven exploration.
Call for Implementation & Research
The SAF encourages developers, researchers, and ML practitioners to:
• experiment with partial or full implementations of DTA
• test its behavior in real Transformer models
• run ablation studies on the Dynamic State Path
• investigate the recurrence rule and stability fields
• explore integration with existing inference stacks (PyTorch, ONNX, CUDA, DirectML)
• evaluate DTA using the proposed stability and coherence metrics
If you build it, test it, stress it, or break it—we want to hear about it.
All such experimentation is welcomed under the SAF Non-Commercial Research License, which allows academic, personal, and exploratory research while prohibiting commercial or governmental deployment.
Implementation feedback, forks, pull requests, and research results are strongly encouraged.
One-Sentence Project Invitation
DTA is an open research direction—if you're interested in pushing Transformers beyond their stateless limitation, you're invited to build it, test it, and help discover what it can (or can't) do.
