By treating language modeling as a continual learning problem, the TTT-E2E architecture achieves the accuracy of full-attention Transformers on 128k context tasks while matching the speed of linear models.
Meta’s VL-JEPA outperforms massive vision-language models on world modeling tasks by learning to predict "thought vectors" instead of text tokens.
The key to solving complex reasoning isn't stacking more transformer layers, but refining the "thought process" through efficient recurrent loops.
Most systems break at 100x growth. Real scalability depends on architecture, data quality, and organizational design, not just writing better code.
Google didn’t reveal a lot of information about its Gemini 3 Flash model. So we had to speculate a lot on what is going on under the hood.
As the industry shifts from chatbots to multi-agent workflows, Nvidia's Nemotron 3 offers a blueprint for efficient, long-context reasoning.
AI labs are racing to overtake each other on key industry benchmarks. But this intense race has stripped the benchmarks of most of their value.
WALT abstracts away the chaos of dynamic layouts, allowing AI to focus on high-level planning instead of low-level clicks.
The verified solution achieves 54% accuracy on the semi-private test set, outperforming Gemini 3 Deep Think at less than half the cost.
OpenAI’s problem is not that it doesn't have the best model anymore but that the general feeling is that it has fallen behind.