The field of scientific research is shifting towards a more nuanced understanding of evaluation and peer review. Recent studies have highlighted the limitations of relying solely on citation metrics, and the importance of considering other factors such as the quality of writing, author-reviewer interactions, and the dynamics of reviewer disagreement. The development of new frameworks and tools, such as debiased pairwise learning and modular LaTeX frameworks, is also improving the efficiency and clarity of scientific communication. Noteworthy papers in this area include NAIPv2, which presents a debiased and efficient framework for paper quality estimation, and Zero-shot reasoning for simulating scholarly peer-review, which investigates a deterministic simulation framework for evaluating AI-generated peer review reports. These advancements have the potential to increase the fairness, transparency, and reliability of scientific research and peer review.