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…
Language modeling plays a foundational role in natural language processing, enabling machines to predict and generate text that resembles human language. These models have evolved significantly, beginning with statistical methods and progressing through neural architectures to today’s large-scale transformer-based systems. At the center of many applications, such as chatbots, translation tools, and text completion engines,…
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…
The Challenge of Multimodal Reasoning Recent breakthroughs in text-based language models, such as DeepSeek-R1, have demonstrated that RL can aid in developing strong reasoning skills. Motivated by this, researchers have attempted to apply the same RL techniques to MLLMs to enhance their ability to reason across both visual and textual inputs. However, these attempts haven’t…
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…
The Shift in Agentic AI System Needs LLMs are widely admired for their human-like capabilities and conversational skills. However, with the rapid growth of agentic AI systems, LLMs are increasingly being utilized for repetitive, specialized tasks. This shift is gaining momentum—over half of major IT companies now use AI agents, with significant funding and projected…
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…
Autoencoders and the Latent Space Neural networks are designed to learn compressed representations of high-dimensional data, and autoencoders (AEs) are a widely-used example of such models. These systems employ an encoder-decoder structure to project data into a low-dimensional latent space and then reconstruct it back to its original form. In this latent space, the patterns…