Synthetic Survival Control: Extending Synthetic Controls for "When-If" Decision

This study evaluates OpenAI's o1-preview large language model, highlighting its performance across various complex reasoning tasks in fields such as computer science, mathematics, and medicine. The model achieved a success rate of 83.3% in competitive programming, excelled in generating radiology reports, and demonstrated 100% accuracy in high school-level math tasks. Its advanced natural language inference capabilities further underscore its potential in diverse applications.

Large language models (LLMs) are increasingly utilized in finance and economics, where their ability to understand chronology is critical. A study tested this capability through various chronological ordering tasks, revealing that while models like GPT-4.1 and GPT-5 can maintain local order, they struggle with creating a consistent global timeline. The findings indicate a significant drop in exact match rates as task complexity increases, particularly in conditional sorting tasks, highlighting inherent limitations in LLMs' chronological reasoning.

DomainCQA is a proposed framework aimed at enhancing Chart Question Answering (CQA) by focusing on both visual comprehension and knowledge-intensive reasoning. Current benchmarks primarily assess superficial parsing of chart data, neglecting deeper scientific reasoning. The framework has been applied to astronomy, resulting in AstroChart, which includes 1,690 QA pairs across 482 charts. This benchmark reveals significant weaknesses in fine-grained perception, numerical reasoning, and domain knowledge integration among 21 Multimodal Large Language Models (MLLMs).