AI Shorts

Challenges in Simulating High-Speed Flows with Neural Solvers Modeling high-speed fluid flows, such as those in supersonic or hypersonic regimes, poses unique challenges due to the rapid changes associated with shock waves and expansion fans. Unlike low-speed flows, where fixed time steps work well, these fast-moving flows require adaptive time stepping to capture small-scale dynamics…

The debate around the reasoning capabilities of Large Reasoning Models (LRMs) has been recently invigorated by two prominent yet conflicting papers: Apple’s “Illusion of Thinking” and Anthropic’s rebuttal titled “The Illusion of the Illusion of Thinking”. Apple’s paper claims fundamental limits in LRMs’ reasoning abilities, while Anthropic argues these claims stem from evaluation shortcomings rather…

Multimodal LLMs: Expanding Capabilities Across Text and Vision Expanding large language models (LLMs) to handle multiple modalities, particularly images and text, has enabled the development of more interactive and intuitive AI systems. Multimodal LLMs (MLLMs) can interpret visuals, answer questions about images, and engage in dialogues that include both text and pictures. Their ability to…

Introduction: Understanding Generalization in Deep Generative Models Deep generative models, including diffusion and flow matching, have shown outstanding performance in synthesizing realistic multi-modal content across images, audio, video, and text. However, the generalization capabilities and underlying mechanisms of these models are challenging in deep generative modeling. The core challenge includes understanding whether generative models truly…

The Challenge of Long-Context Reasoning in AI Models Large reasoning models are not only designed to understand language but are also structured to think through multi-step processes that require prolonged attention spans and contextual comprehension. As the expectations from AI grow, especially in real-world and software development environments, researchers have sought architectures that can handle…

AI institutions develop heterogeneous models for specific tasks but face data scarcity challenges during training. Traditional Federated Learning (FL) supports only homogeneous model collaboration, which needs identical architectures across all clients. However, clients develop model architectures for their unique requirements. Moreover, sharing effort-intensive locally trained models contains intellectual property and reduces participants’ interest in engaging…

Introduction: The Need for Efficient RL in LRMs Reinforcement Learning RL is increasingly used to enhance LLMs, especially for reasoning tasks. These models, known as Large Reasoning Models (LRMs), generate intermediate “thinking” steps before providing final answers, thereby improving performance on complex problems such as math and coding. However, training LRMs with RL at scale…