FFTNet introduces a novel approach to sequence processing using Fast Fourier Transform, achieving O(n log n) complexity compared to traditional self-attention's quadratic complexity. The framework employs spectral filtering and modReLU activation to efficiently capture long-range dependencies, demonstrating superior performance on Long Range Arena and ImageNet benchmarks.
Figure introduces Helix, a groundbreaking Vision-Language-Action model capable of controlling humanoid robot upper bodies through natural language commands. The system uniquely combines high-speed continuous control with multi-robot collaboration capabilities, operating entirely on embedded GPUs. Helix demonstrates remarkable ability to manipulate thousands of novel objects without prior training, marking a significant advancement in scalable robotics.
Various alternative architectures to Transformers are being explored, with MAMBA showing promise through faster inference and lower compute costs, performing on par with transformers up to 7B parameters. Researchers are investigating recurrent architectures, state-space models, and efficient attention mechanisms, while debating the future direction of foundation models.
A novel Large Memory Model (LM2) architecture enhances Transformers with an auxiliary memory module, significantly outperforming existing models in multi-hop inference and numerical reasoning tasks. The model demonstrates a 37.1% improvement over RMT and 86.3% over Llama-3.2 on the BABILong benchmark while maintaining strong performance on general tasks.
NVIDIA engineers utilized the DeepSeek-R1 model with inference-time scaling to automatically generate optimized GPU attention kernels, achieving results that sometimes surpassed human-engineered solutions. The experiment demonstrates how AI models can leverage additional computational resources during inference to evaluate multiple outcomes and select optimal solutions for complex programming tasks.
Transformers' extraordinary learning capabilities allow them to master skills through simple observation of related tasks, showcasing the potential of emergent behavior in AI. Recent studies demonstrate that transformer models can learn complex skills without explicit training, revealing profound implications for future AI development and understanding.
Andrej Karpathy's deep dive into LLMs covers the complete lifecycle from pretraining to post-training, explaining tokenization, neural network architectures, and fine-tuning processes. The comprehensive guide explores how LLMs process information, handle hallucinations, and utilize reinforcement learning to improve performance and reasoning capabilities.
An experimental project applying large-scale Reinforcement Learning techniques to computer usage scenarios, utilizing neural reward models to validate agent actions. The system implements a three-step cycle extending ReACT into reinforcement learning, with multiple training stages focused on developing reasoning skills for computer interaction.
A comprehensive exploration of Reinforcement Learning (RL) through implementing a Pong-playing AI using Policy Gradients, demonstrating how neural networks can learn complex behaviors from raw pixel inputs with minimal preprocessing and assumptions.