The recent research in table comprehension and multi-modal reasoning has seen significant advancements, particularly in addressing the complexities of real-world data integration and reasoning. A notable trend is the development of benchmarks that more accurately reflect the challenges faced in practical applications, such as financial reports, scientific tables, and legal documents. These benchmarks are designed to evaluate models not just on isolated tasks but on holistic comprehension and multi-step reasoning, which are crucial for effective decision-making in high-stakes industries. Innovations in model architectures, such as the integration of domain-specific tools and interpretable approaches, are also advancing the field, offering more accurate and transparent solutions. Notably, the introduction of multi-scale benchmarks and meta-operations for table reasoning is pushing the boundaries of what models can achieve, highlighting the need for continued research into more complex and diverse data scenarios. In summary, the field is moving towards more realistic and challenging evaluations, coupled with more sophisticated and interpretable model designs.