Gio (Jiwoong) Choi

Keywords: Automated Hypothesis Generation, Scientific Evaluation, Large Language Models
What constitutes good science remains a longstanding question in both philosophy and practice. Traditional peer review, for instance, has been critiqued for subjectivity, inconsistency, and potential bias. Recent advances in large language models (LLMs) offer a novel opportunity to re-examine this question at scale. Here, we analyze a dataset of approximately 27K papers submitted to 45 computer science conferences, paired on review scores to create clear distinctions in perceived quality. Rather than manually defining the criteria of “good” science, we task LLMs with iteratively proposing, testing, and refining hypotheses that explain why one paper might be judged as stronger than another. This yields a final pool of 20 orthogonal hypotheses with high coverage of pairs. Throughout this abductive reasoning process, the LLM’s initial “normative” prior beliefs (e.g., a good paper has high novelty) are updated into a posterior that reflects more professional-science criteria (e.g., a good paper tells a good story). LLMs could serve as powerful tools for uncovering latent patterns in how experts judge scientific work. Nevertheless, challenges remain. Interpretability is a critical bottleneck: while the iterative process yields human-understandable hypotheses, it relies on opaque LLM reasoning under the hood. In addition, substantial progress is still needed in guiding LLMs and humans toward a clearer understanding of what constitutes truly valuable science.

This paper examines the "Board and Ownership and Control" Key Issue within the MSCI ESG Ratings Corporate Governance Theme, with a focus on board independence and adherence to the one-share-one-vote (OSOV) principle. Our analysis reveals significant governance challenges among Korean companies. Notably, less than half of the directors on Korean company boards are independent, a figure significantly lower than the global average of 66%. Additionally, 82% of Korean companies were flagged for Related Party Transactions (RPT), with smaller firms exhibiting lower board independence. Furthermore, Korean companies that deviated from the OSOV principle demonstrated weaker financial performance, with both return-on-equity (ROE) and price-to-book ratio (PBR) falling below the market average. These findings underscore the need for enhanced governance practices within Korean corporations to align more closely with global standards.

- Despite high primary and lower secondary education completion rates in ASEAN, only 64 percent of students complete upper secondary education, with boys, particularly in rural areas, being more likely to drop out due to economic barriers and opportunity costs. While girls tend to stay in school longer, challenges such as inadequate access to employment opportunities, poor educational infrastructure, and disparities between urban and rural areas persist, highlighting the need for increased investments in education across the region.
- Adolescent birth rates in South-East Asia have decreased from 41 to 35 per 1,000 between 2015 and 2024, with factors such as delayed marriage, access to contraceptives, and education contributing to this decline. However, disparities in education infrastructure, particularly in rural areas, remain a challenge, with limited access to basic facilities like sanitation and water, which increases the likelihood of teenage pregnancy and school dropout among rural girls.
- Despite South-East Asia being one of the world’s safest regions with a homicide rate of 1.8 per 100,000 people, a growing sense of insecurity, particularly among women, has emerged due to factors like COVID-19, economic disruptions, and crime, highlighting the need for enhanced law enforcement and inclusive security approaches.
- Over the past decade, official development assistance (ODA) for gender equality in the ASEAN region has increased significantly, with 47% of all ODA in 2022 supporting gender-focused initiatives, though investments directly targeting gender equality have declined, highlighting the need for continued and expanded funding to sustain progress in areas like women’s participation, violence reduction, and the gender-environment nexus.

This paper proposes a robust visual SLAM and a path planning algorithm for autonomous vehicles in the outdoor environment. The consideration of the outdoor characteristics was essential in both SLAM and path planning processes. This study can be used when it is necessary to know the exact appearance of the environment due to the impossibility of observing the environment through a satellite map, e.g., inside a forest. The visual SLAM system was developed using GPS data in consideration of the deterioration of camera recognition performance outdoors. The GPS data was inserted into every multi-thread of visual SLAM, which are Camera Tracking, Local Mapping, and Loop Closing. It enhanced the accuracy of the map and saved computational power by preventing useless calculations. In the path planning part, our method divided the path based on the stability of the roads. When determining the optimal path, the stability of the road and the driving time were considered, and the weight was assigned based on the GPS data.