Designing GUI agents that perform human-like tasks on graphical user interfaces faces a critical obstacle: collecting high-quality trajectory data for training. Existing methods depend on expensive and time-consuming human supervision or on generating synthetic data, which can hardly reflect the diversity and dynamics in the real world. Those constraints significantly limit the GUI agents’ scalability…
Power distribution systems are often conceptualized as optimization models. While optimizing agents to perform tasks works well for systems with limited checkpoints, things begin to go out of hand when heuristics tackle multiple tasks and agents. Scaling dramatically increases the complexity of assignment problems, often NP-hard and nonlinear. Optimization methods become the white elephants in…
The development of large language models (LLMs) has significantly advanced artificial intelligence (AI) across various fields. Among these advancements, mobile GUI agents—designed to perform tasks autonomously on smartphones—show considerable potential. However, evaluating these agents poses notable challenges. Current datasets and benchmarks often rely on static frame evaluations, which provide snapshots of app interfaces for agents…
Evaluating the real-world applicability of large language models (LLMs) is essential to guide their integration into practical use cases. One key challenge in assessing LLMs is their tendency to exploit fixed datasets during testing, leading to inflated performance metrics. Static evaluation frameworks often fail to determine a model’s ability to adapt to feedback or provide…
Proteins, the essential molecular machinery of life, play a central role in numerous biological processes. Decoding their intricate sequence, structure, and function (SSF) is a fundamental pursuit in biochemistry, molecular biology, and drug development. Understanding the interplay between these three aspects is crucial for uncovering the principles of life at a molecular level. Computational tools…
Large language models (LLMs) have brought significant progress to AI applications, including code generation. However, evaluating their true capabilities is not straightforward. Existing benchmarks, such as LiveCodeBench and USACO, have limitations. They lack robust private test cases, do not support specialized judgment systems, and often work with inconsistent execution environments. These gaps make it challenging…
Inspired by the brain, neural networks are essential for recognizing images and processing language. These networks rely on activation functions, which enable them to learn complex patterns. However, many activation functions face challenges. Some struggle with vanishing gradients, which slows learning in deep networks, while others suffer from “dead neurons,” where certain parts of the…
The self-attention mechanism is a building block of transformer architectures that faces huge challenges both in the theoretical foundations and practical implementation. Despite such successes in natural language processing, computer vision, and other areas, their development often relies on heuristic approaches, limiting interpretability and scalability. Self-attention mechanisms are also vulnerable to data corruption and adversarial…
In today’s fast-paced world, staying organized is crucial for productivity, especially for professionals handling complex tasks like financial management. AI-powered note-taking tools have revolutionized how we manage, structure, and access information. These tools not only make note-taking easier but also provide insights, automate tasks, and enhance collaboration. Here’s a list of the top 25 AI…
Diffusion Policies in Imitation Learning (IL) can generate diverse agent behaviors, but as models grow in size and capability, their computational demands increase, leading to slower training and inference. This challenges real-time applications, especially in environments with limited computing power, like mobile robots. These policies need many parameters and denoising steps and, thus, are unsuitable…