Enhancing Privately Deployed AI Models: A Sampling-Based Search Approach
Enhancing Privately Deployed AI Models: A Sampling-Based Search Approach Introduction Privately deployed AI models—used in secure enterprise environments or edge devices—face unique limitations. Unlike their cloud-based counterparts that benefit from extensive computational resources, these models often operate under tight constraints. As a result, they struggle with inference-time optimization, accurate self-verification, and scalable reasoning. These issues can diminish trust and reliability in critical domains like finance, law, and healthcare. How can we boost the accuracy and robustness of such models without fundamentally redesigning them or relying on cloud support? ...