The field of research synthesis is moving towards the development of more comprehensive and reliable methods for evaluating and generating research reports. This is driven by the need for rigorous standards of factual accuracy and comprehensiveness in research tasks. Recent work has focused on creating systematic benchmarks and evaluation frameworks to assess the quality of research reports generated by large language models. Additionally, there is a growing emphasis on mitigating bias in source selection and promoting fair and balanced knowledge retrieval. These developments have the potential to significantly advance the field of research synthesis and improve the overall quality of research reports. Noteworthy papers include: ReportBench, which proposes a systematic benchmark for evaluating the content quality of research reports generated by large language models. SurveyGen, which presents a large-scale dataset for evaluating the performance of scientific survey generation systems. Bias Mitigation Agent, which introduces a novel multi-agent system for optimizing source selection to ensure fair and balanced knowledge retrieval. DeepScholar-Bench, which introduces a live benchmark and automated evaluation framework for generative research synthesis.