Scientific research is often constrained by resource limitations and time-intensive processes. Tasks such as hypothesis testing, data analysis, and report writing demand significant effort, leaving little room for exploring multiple ideas simultaneously. The increasing complexity of research topics further compounds these issues, requiring a blend of domain expertise and technical skills that may not always… →

Large Language Models (LLMs) aim to align with human preferences, ensuring reliable and trustworthy decision-making. However, these models acquire biases, logical leaps, and hallucinations, rendering them invalid and harmless for critical tasks involving logical thinking. Logical consistency problems make it impossible to develop logically consistent LLMs. They also use temporal reasoning, optimization, and automated systems,… →

Owing to the advent of Artificial Intelligence (AI), the software industry has been leveraging Large Language Models (LLMs) for code completion, debugging, and generating test cases. However, LLMs follow a generic approach when developing test cases for a different software, which prevents them from considering the software’s unique architecture, user requirements and potential edge cases.… →

Large Language Models (LLMs) have significantly advanced artificial intelligence, particularly in natural language understanding and generation. However, these models encounter difficulties with complex reasoning tasks, especially those requiring multi-step, non-linear processes. While traditional Chain-of-Thought (CoT) approaches, which promote step-by-step reasoning, improve performance on simpler tasks, they often fall short in addressing more intricate problems. This… →

Artificial intelligence research has steadily advanced toward creating systems capable of complex reasoning. Multimodal large language models (MLLMs) represent a significant development in this journey, combining the ability to process text and visual data. These systems can address intricate challenges like mathematical problems or reasoning through diagrams. By enabling AI to bridge the gap between… →

The generation of synthetic tabular data has become increasingly crucial in fields like healthcare and financial services, where privacy concerns often restrict the use of real-world data. While autoregressive transformers, masked transformers, and diffusion models with transformers, have shown significant success in generating high-quality synthetic data with strong fidelity, utility, and privacy guarantees, they face… →