T-Scan is a technique designed to inspect and visualize the internal activations of transformer models, offering a reproducible measurement and logging method that can be extended or rendered using various tools. The project includes scripts for downloading a model, running a baseline scan, and a Gradio-based interface for causal intervention, allowing users to perturb up to three dimensions and compare baseline versus perturbed behavior. Logs are consistently formatted to facilitate easy comparison and visualization, though the project does not provide a polished visualization tool, leaving rendering to the user's preference. The method is model-agnostic but currently targets the Qwen 2.5 3B model for accessibility, aiming to assist those in interpretability research. This matters because it provides a flexible and extendable framework for understanding transformer internals, which is crucial for advancing AI interpretability and transparency.
Transformers are often overutilized as universal execution engines, leading to inefficiencies. A proposed meaning-first execution framework separates semantic proposal from model execution, enabling conditional inference only when necessary. This approach allows a significant reduction in transformer calls without affecting the accuracy of the results, indicating that many efficiency constraints are architectural rather than inherent to the models themselves. This model-agnostic method could enhance the efficiency of existing transformers by reducing unnecessary processing. Understanding and implementing such frameworks can lead to more efficient AI systems, reducing computational costs and energy consumption.