Large language models (LLMs) have rapidly advanced multimodal large language models (LMMs), particularly in vision-language tasks. Videos represent complex, information-rich sources crucial for understanding real-world scenarios. However, current video-language models encounter significant challenges in temporal localization and precise moment detection. Despite extensive training in video captioning and question-answering datasets, these models struggle to identify and… →

Medprompt, a run-time steering strategy, demonstrates the potential of guiding general-purpose LLMs to achieve state-of-the-art performance in specialized domains like medicine. By employing structured, multi-step prompting techniques such as chain-of-thought (CoT) reasoning, curated few-shot examples, and choice-shuffle ensembling, Medprompt bridges the gap between generalist and domain-specific models. This approach significantly enhances performance on medical benchmarks… →

Understanding protein sequences and their functions has always been a challenging aspect of protein research. Proteins, often described as the building blocks of life, are made up of long, complex sequences that determine their roles in biological systems. Despite advancements in computational biology, making sense of these sequences in a meaningful way is still a… →

Astronomical research has transformed dramatically, evolving from limited observational capabilities to sophisticated data collection systems that capture cosmic phenomena with unprecedented precision. Modern telescopes now generate massive datasets spanning multiple wavelengths, revealing intricate details of celestial objects. The current astronomical landscape produces an astounding volume of scientific data, with observational technologies capturing everything from minute… →