We thank all reviewers for their constructive feedback! We address reviewers comments below, and will incorporate all feedback. This explains why SD outperforms LS. Please refer to our response to [R3] for discussion on CD. One can alternatively compute the variance of prediction confidence.

We would like to emphasize again the main contribution of our paper. To Reviewer 1: Thanks for the citations and the correction you provided. In our final submission, we will cite [4] We will also correct all the items mentioned in SPECIFIC REMARKS/TYPOS. It also has many other applications such as volume computation and bandit optimization. The preliminary results are attached.

In most real-world applications of artificial intelligence, the distributions of the data and the goals of the learners tend to change over time. The Probably Approximately Correct (PAC) learning framework, which underpins most machine learning algorithms, fails to account for dynamic data distributions and evolving objectives, often resulting in suboptimal performance. Prospective learning is a recently introduced mathematical framework that overcomes some of these limitations. We build on this framework to present preliminary results that improve the algorithm and numerical results, and extend prospective learning to sequential decision-making scenarios, specifically foraging. Code is available at: https://github.com/neurodata/prolearn2.

To support junior and senior architects, I propose developing a new architecture creation method that leverages LLMs' evolving capabilities to support the architect. This method involves the architect's close collaboration with LLM-fueled tooling over the whole process. The architect is guided through Domain Model creation, Use Case specification, architectural decisions, and architecture evaluation. While the architect can take complete control of the process and the results, and use the tooling as a building set, they can follow the intended process for maximum tooling support. The preliminary results suggest the feasibility of this process and indicate major time savings for the architect.