Google researchers have found that scaling laws similar to those for large language models apply to wearable sensor data, with validation loss scaling predictably with compute, model size, and data si…
AI teams often hit a 'latent capability ceiling' where larger models no longer improve task accuracy, with scaling data showing diminishing returns beyond 100B parameters and 61% of tasks exhibiting n…
A developer built a novel triple-hybrid LLM combining Mamba, Attention, and a 32-expert Mixture of Experts architecture from scratch for approximately $50, completing Titan v1 and the first training c…
Microsoft's MAI-Thinking-1 report introduces an "efficiency gain" (EG) metric that compares candidate model architectures against a baseline by measuring how much compute is needed to reach the same l…
Researchers have developed a conditional scaling law that predicts optimal large language model architectures for inference efficiency, training over 200 models from 80 million to 3 billion parameters…
A new analysis argues that the era of AI scaling producing clearly better models is ending, with frontier labs facing exponentially increasing costs for linear returns. The author claims that the inte…
Amazon researchers have developed a new scaling law that connects large language model architecture directly to inference efficiency, enabling faster text generation without sacrificing accuracy. The …