This paper introduces a parameterization framework for controlling conversation quality in large language models. We explore nine key parameters across six dimensions that enable precise specification of dialogue properties. Through experiments with state-of-the-art LLMs, we demonstrate that parameter-based control produces statistically significant differences in generated conversation properties. Our approach addresses challenges in conversation generation, including topic coherence, knowledge progression, character consistency, and control granularity. The framework provides a standardized method for conversation quality control with applications in education, therapy, customer service, and entertainment. Future work will focus on implementing additional parameters through architectural modifications and developing benchmark datasets for evaluation.

Nothing can take in more information and process it faster and more accurately than the human visual cortex…until now. Humans classify complex objects at speeds up to 27Hz, with the brain processing 580 megapixels of data in as little as 13 milliseconds. While conventional LiDAR sensors on autonomous vehicles average around a 10Hz frame rate and revisit rate, iDAR sensors can achieve a frame rate in excess of 100Hz ( 3x human vision), and an object revisit rate of 500Hz. A single interrogation point rarely delivers sufficient confidence – it is only suggestive. That's why LiDAR systems must capture multiple detects of the same object to fully comprehend it, making the speed of subsequent interrogations/detects (the object revisit rate) significantly more critical to autonomous vehicle safety than frame rate alone.