This study presents a framework for dynamic assortment selection and pricing using a censored multinomial logit choice model, where sellers can optimize product offerings and prices based on buyer preferences and valuations. By employing a Lower Confidence Bound pricing strategy alongside Upper Confidence Bound or Thompson Sampling approaches, the proposed algorithms achieve significant regret bounds, which are validated through simulations.

Updates to the Gemini 2.5 model family have been announced, including the general availability of Gemini 2.5 Pro and Flash, along with a new Flash-Lite model in preview. The models enhance performance through improved reasoning capabilities and offer flexible pricing structures, particularly for cost-sensitive applications. Gemini 2.5 Pro continues to see high demand and is positioned for advanced tasks like coding.