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… →

Transforming language models into effective red teamers is not without its challenges. Modern large language models have transformed the way we interact with technology, yet they still struggle with preventing the generation of harmful content. Efforts such as refusal training help these models deny risky requests, but even these safeguards can be bypassed with carefully… →

Artificial intelligence in multi-agent environments has made significant strides, particularly in reinforcement learning. One of the core challenges in this domain is developing AI agents capable of communicating effectively through natural language. This is particularly critical in settings where each agent has only partial visibility of the environment, making knowledge-sharing essential for achieving collective goals.… →

Recent discussions on AI safety increasingly link it to existential risks posed by advanced AI, suggesting that addressing safety inherently involves considering catastrophic scenarios. However, this perspective has drawbacks: it may exclude researchers with different approaches, mislead the public into thinking AI safety is solely about existential threats, and create resistance among skeptics. As AI… →