# Apr 13, 2025

## Google is winning on every AI front

- **Google DeepMind's Gemini 2.5 Pro** is currently the leading AI model, outperforming competitors like OpenAI and Anthropic across multiple benchmarks, including [LMArena](https://lmarena.ai/?leaderboard) and [Humanity's Last Exam](https://scale.com/leaderboard/humanitys_last_exam).

- **Google will let companies run Gemini models in their own data centers**, enhancing data control and security, with early access slated for the **third quarter** of 2025.

- **OmniSVG** is the **first end-to-end multimodal SVG generator** utilizing pre-trained Vision-Language Models (VLMs), enabling the creation of both simple icons and complex anime characters.

- **Skywork-OR1** introduces a series of advanced reasoning models, including **`Skywork-OR1-Math-7B`**, optimized for mathematical tasks, achieving a score of **69.8** on AIME24, significantly outperforming similar-sized models.

- **Google's decision to allow enterprises to self-host SOTA models** marks a significant shift in the landscape of AI, enhancing **data privacy** and control for businesses, akin to Mistral's existing model.

- **d1** is a novel framework that enhances **reasoning capabilities** in diffusion-based large language models (dLLMs) through a combination of **supervised finetuning** and a unique **critic-free RL algorithm** called diffu-GRPO.

- **The ML paradox** reveals that models excelling in metrics like accuracy can lead to **catastrophic real-world failures**, such as harmful medical recommendations or erratic self-driving behavior.

- **Adding new vocab tokens** to LLMs during instruction-tuning has proven **ineffective**, as models using the base tokenizer show superior validation losses and output quality compared to those with modified tokenizers.

- **BSE (Bramble Semantic Engine)** is a **semantic compressor** that efficiently transforms natural inputs into **low-dimensional structured representations** for language, image, and audio, enhancing preprocessing for LLMs.

- The proposed **residual activation function** `f(x) = x + α · g(sin²(πx / 2))` aims to enhance **normalization-free deep networks**, potentially improving performance in MLPs and Transformers.

- **AI-native runtime** enables snapshot-loading of **50+ LLMs** (13B–65B) in **2–5 seconds**, allowing dynamic execution without constant memory residency.

- **ICML 2025: A Shift Toward Correctness Over SOTA?**