You Only Need Your Transformer 25% of the Time: Meaning-First Execution for Eliminating Unnecessary Inference

This paper argues that transformers are being overused as universal execution engines.

I propose a meaning-first execution framework that decouples semantic proposal from model execution, allowing inference to be conditionally invoked only when needed.

The result is that a large fraction of transformer calls can be skipped without changing correctness on invoked cases, suggesting many current efficiency limits are architectural rather than model-intrinsic.

The work is model-agnostic and sits above existing transformers.

Feedback welcome, especially around routing guarantees and failure modes.