Inferential Strategy

Inferential strategy research focuses on optimizing how machine learning models, particularly large language models (LLMs), arrive at conclusions, emphasizing both accuracy and computational efficiency. Current work investigates diverse inference methods, including tree search algorithms and Bayesian approaches, analyzing their effectiveness across different model architectures and scales, and comparing them to human reasoning strategies. This research aims to improve the reliability and explainability of AI systems, leading to more robust and trustworthy applications across various domains, from problem-solving to scientific discovery.