Researchers from Google DeepMind introduce the concept of "Socratic learning." This refers to a form of recursive self-improvement in artificial intelligence that significantly enhances performance beyond the initial data or knowledge available to the system, as well as a practical framework to […]

Apple researchers conducted a systematic study of the computational bottlenecks and cost-efficiency of training SLMs. Their work evaluates training strategies across diverse cloud infrastructure setups, offering practical insights for improving efficiency and reducing costs. The post […]

OpenAI researchers introduces TrigFlow, a simplified theoretical framework that identifies the key causes of training instability of consistency models and addresses them with novel improvements in diffusion process parameterization, network architecture, and training objectives. The post Redefines […]

In a new paper Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models, an NVIDIA research team introduces Edify Image—a suite of pixel-based diffusion models that achieve high-resolution image synthesis with exceptional control and precision. The post Precision in […]

In a new paper Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces, a Meta research team presents Dualformer, a single Transformer model that merges both fast and slow reasoning modes within a unified framework. The post Meta’s Dualformer: Bridging Fast […]

An NVIDIA research team introduces OMCAT: Omni Context Aware Transformer in their new paper, presenting both OCTAV, a unique dataset aimed at capturing event transitions across audio and video, and OMCAT, a model that employs RoTE (Rotary Time Embeddings). The post NVIDIA’s OMCAT: A Breakthrough […]

A Stanford University research team presents Tutor CoPilot, a new model that offers expert-level guidance to tutors in real time. This study is the first of its kind—a randomized controlled trial testing a Human-AI system in live tutoring scenarios. The post Stanford U’s Tutor CoPilot […]

A research team introduces a novel approach called Induction-head ngram models (Induction-Gram). This technique merges the interpretability and efficiency of n-gram models with insights from neural LLMs to enhance language modeling performance. The post Bridging the Gap: Induction-Head Ngram Models […]

A research team from DeepMind and Chicago University presents a novel approach to Reinforcement Learning from Human Feedback. The proposed eva introduces a flexible, scalable framework that leverages any RLHF algorithm to drive more effective alignment with human values The post Self-Evolving […]

A research team from Google DeepMind and the University of Alberta presents evidence that transformer-based LLMs using autoregressive decoding can indeed support universal computation without any external adjustments or modifications to model weights. The post Unlocking Turing Completeness: How […]