This research introduces a structured latency perturbation technique that optimizes computational pathways in large language models, making them faster without sacrificing performance.

• Creates dynamic suppression of redundant activations
• Preserves generative fidelity while reducing resource usage
• Enables more responsive real-time AI applications
• Addresses critical computational efficiency challenges in AI scaling

Engineering Impact: This innovation directly tackles one of the most significant barriers to widespread LLM deployment - the high computational cost and latency of inference, potentially enabling more efficient AI systems for resource-constrained environments.

Structural Latency Perturbation in Large Language Models Through Recursive State Induction