Transforming Human-Computer Interaction with Generative Interfaces Recent advances in generative models are transforming the way we interact with computers, making experiences more natural, adaptive, and personalized. Early interfaces, command-line tools, and static menus were fixed and required users to adapt to the machine. Now, with the rise of LLMs and multimodal AI, users can engage…
Diffusion LLMs as a Paradigm Shift in Code Generation LLMs have revolutionized natural language processing with impressive results across tasks from dialogue to code generation. Masked diffusion models have emerged as an alternative and are scaled up into diffusion-based LLMs such as LLaDA and Dream. This model iteratively refines the entire sequence in parallel, allowing…
Handling questions that involve both natural language and structured tables has become an essential task in building more intelligent and useful AI systems. These systems are often expected to process content that includes diverse data types, such as text mixed with numerical tables, which are commonly found in business documents, research papers, and public reports….
What is included in this article: The limitations of current test-time compute strategies in LLMs.Introduction of Fractional Reasoning (FR) as a training-free, model-agnostic framework.Techniques for latent state manipulation using reasoning prompts and adjustable scaling.Breadth- and depth-based scaling benefits demonstrated across GSM8K, MATH500, and GPQA.Evaluation results showing FR’s superiority over Best-of-N and Majority Vote.Analysis of FR’s…
What is included in this article: Performance breakthroughs – 2x faster inference and 3x faster trainingTechnical architecture – Hybrid design with convolution and attention blocksModel specifications – Three size variants (350M, 700M, 1.2B parameters)Benchmark results – Superior performance compared to similar-sized modelsDeployment optimization – Edge-focused design for various hardwareOpen-source accessibility – Apache 2.0-based licensingMarket implications…
AI-powered video generation is improving at a breathtaking pace. In a short time, we’ve gone from blurry, incoherent clips to generated videos with stunning realism. Yet, for all this progress, a critical capability has been missing: control and Edits While generating a beautiful video is one thing, the ability to professionally and realistically edit it—to…
Hugging Face just released SmolLM3, the latest version of its “Smol” language models, designed to deliver strong multilingual reasoning over long contexts using a compact 3B-parameter architecture. While most high-context capable models typically push beyond 7B parameters, SmolLM3 manages to offer state-of-the-art (SoTA) performance with significantly fewer parameters—making it more cost-efficient and deployable on constrained…
Introduction to Video Diffusion Models and Computational Challenges Diffusion models have made impressive progress in generating high-quality, coherent videos, building on their success in image synthesis. However, handling the extra temporal dimension in videos significantly increases computational demands, especially since self-attention scales poorly with sequence length. This makes it difficult to train or run these…
Understanding Limitations of Current Reward Models Although reward models play a crucial role in Reinforcement Learning from Human Feedback (RLHF), many of today’s top-performing open models still struggle to reflect the full range of complex human preferences. Even with sophisticated training techniques, meaningful progress has been limited. A major reason appears to be the shortcomings…
Optimizing LLMs for Human Alignment Using Reinforcement Learning Large language models often require a further alignment phase to optimize them for human use. In this phase, reinforcement learning plays a central role by enabling models to make decisions based on human feedback or task-based correctness. This fine-tuning allows for the models to align more closely…