Heard about Artificial General Intelligence (AGI)? Meet its auditory counterpart—Audio General Intelligence. With Audio Flamingo 3 (AF3), NVIDIA introduces a major leap in how machines understand and reason about sound. While past models could transcribe speech or classify audio clips, they lacked the ability to interpret audio in a context-rich, human-like way—across speech, ambient sound,…
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…
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…
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…
TLDR: Chai Discovery Team introduces Chai-2, a multimodal AI model that enables zero-shot de novo antibody design. Achieving a 16% hit rate across 52 novel targets using ≤20 candidates per target, Chai-2 outperforms prior methods by over 100x and delivers validated binders in under two weeks—eliminating the need for large-scale screening. In a significant advancement…
Recent research indicates that LLMs, particularly smaller ones, frequently struggle with robust reasoning. They tend to perform well on familiar questions but falter when those same problems are slightly altered, such as changing names or numbers, or adding irrelevant but related information. This weakness, known as poor out-of-distribution (OOD) generalization, results in notable accuracy drops,…
Improving the reasoning capabilities of large language models (LLMs) without architectural changes is a core challenge in advancing AI alignment and usability. Researchers at Meta AI and the University of Washington have introduced ASTRO—Autoregressive Search-Taught Reasoner—a novel post-training framework designed to enhance reasoning in Llama-3.1-70B-Instruct. ASTRO is unique in teaching models to perform in-context search,…