Addressing the evolving challenges in software engineering starts with recognizing that traditional benchmarks often fall short. Real-world freelance software engineering is complex, involving much more than isolated coding tasks. Freelance engineers work on entire codebases, integrate diverse systems, and manage intricate client requirements. Conventional evaluation methods, which typically emphasize unit tests, miss critical aspects such… →

Large language models have demonstrated remarkable problem-solving capabilities and mathematical and logical reasoning. These models have been applied to complex reasoning tasks, including International Mathematical Olympiad (IMO) combinatorics problems, Abstraction and Reasoning Corpus (ARC) puzzles, and Humanity’s Last Exam (HLE) questions. Despite improvements, existing AI models often struggle with high-level problem-solving that requires abstract reasoning,… →

Understanding different data types like text, images, videos, and audio in one model is a big challenge. Large language models that handle all these together struggle to match the performance of models designed for just one type. Training such models is difficult because different data types have different patterns, making it hard to balance accuracy… →

Diffusion models have emerged as a crucial generative AI framework, excelling in tasks such as image synthesis, video generation, text-to-image translation, and molecular design. These models function through two stochastic processes: a forward process that incrementally adds noise to data, converting it into Gaussian noise, and a reverse process that reconstructs samples by learning to… →